
Cardiology Science For People
- 31 installs
- 5 repo stars
- Updated June 18, 2026
- drshailesh88/integrated_content_os
Write scientifically rigorous cardiology content for general audiences in plain English, using stories over statistics while keeping full accuracy.
About
This skill writes accurate cardiology science for lay audiences, translating trials and research into plain-English stories an 8th grader can follow without oversimplifying. Cardiologists use it to create public-facing content that stays scientifically correct with PubMed verification.
- Leads with the human story and translates statistics into meaning
- Maintains full rigor with PubMed citations while avoiding jargon and acronyms
Cardiology Science For People by the numbers
- 31 all-time installs (skills.sh)
- Ranked #1,404 of 1,879 Marketing & SEO skills by installs in the Skillselion catalog
- Data as of Jul 29, 2026 (Skillselion catalog sync)
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| Installs | 31 |
|---|---|
| repo stars | ★ 5 |
| Last updated | June 18, 2026 |
| Repository | drshailesh88/integrated_content_os ↗ |
What it does
Write scientifically rigorous cardiology content for general audiences in plain English, using stories over statistics while keeping full accuracy.
Files
Cardiology Science for People
Write rigorous cardiology science that real people actually want to read. Same accuracy as an academic editorial. Zero intimidation.
Core Philosophy
The problem you're solving: Academic writing says correct things in ways that require a medical degree to understand. Dumbed-down content oversimplifies to the point of being wrong.
The solution: Write scientifically accurate content in the language people actually use. An 8th grader should understand it. A cardiologist should find no errors.
What "for people" means:
- Stories, not statistics
- What it means for them, not what the trial showed
- Names of conditions, not trial acronyms
- Explanations that stand alone, not explanations requiring explanations
The Three Golden Rules
1. Lead with the Human Story
Never start with: "The STEP-HFpEF trial randomized 529 patients..."
Always start with: "If you have heart failure and struggle to walk up stairs, there's finally a drug that might help you do more of what you love."
The trial is evidence. The story is why anyone should care.
2. Translate Statistics to Meaning
Never write: "The hazard ratio was 0.65 (95% CI 0.51-0.82), representing a 35% relative risk reduction."
Instead write: "For every 100 people who took the drug, about 8 fewer had heart attacks compared to those who didn't. That's a meaningful difference—roughly 1 in 12 people benefited."
Always convert to:
- "X out of 100 people..." (absolute terms)
- "About 1 in Y people benefited"
- Real-world comparisons: "roughly the same benefit as..."
- Time frames that matter: "over the next 5 years..."
3. Background the Evidence, Foreground the Understanding
Academic style: "The DAPA-HF trial (McMurray et al., NEJM 2019) demonstrated that dapagliflozin reduced the composite of worsening heart failure or cardiovascular death by 26% (HR 0.74, 95% CI 0.65-0.85)."
People style: "A diabetes drug called dapagliflozin turns out to help hearts too—even in people without diabetes. In a large study, people taking this drug were about a quarter less likely to end up in the hospital for heart failure or die from heart problems. Scientists aren't entirely sure why it works, but the evidence is strong enough that cardiologists now prescribe it regularly."
The citation goes in your references section. The reader gets the understanding.
Writing Process
Step 1: Research (Same Rigor as Editorial Skill)
Use PubMed MCP exactly as you would for academic writing:
PubMed:search_articlesfor finding trials and evidencePubMed:get_article_metadatafor detailsPubMed:get_full_text_articlewhen available
Target: 5-8 solid references from major journals (NEJM, JAMA, Lancet, JACC, Circulation, EHJ).
Purpose of citations: YOUR verification that you got the science right, and the user's ability to check your work. NOT to impress readers.
Step 2: Extract the Core Story
Before writing, answer:
1. What's the one thing readers need to understand?
- Not what the trial showed. What it MEANS.
2. Why should someone care?
- Not "this is important because..."
- What changes in their life, their risk, their choices?
3. What's the story arc?
- What was the problem before?
- What did we discover?
- What's different now?
4. What's the "so what" for a reader's life?
- Should they ask their doctor about something?
- Should they change a behavior?
- Should they feel relieved or concerned?
Step 3: Write for Understanding
Structure (Flexible—Narrative Flow Over Sections)
Unlike the rigid 7-section editorial, structure should serve the story:
Option A: Problem → Discovery → Meaning
- Start with a relatable problem ("Many people with heart failure can barely walk to the mailbox")
- Introduce the discovery as a story ("Then scientists tried something unexpected...")
- Land on what it means for the reader ("For you, this means...")
Option B: Surprising Fact → Explanation → Implications
- Hook with something unexpected ("A diabetes drug is now one of the best heart failure treatments")
- Explain how we got here
- Connect to reader's life
Option C: Person's Story → Science → Takeaway
- Start with a composite patient scenario
- Weave in the science
- End with actionable understanding
Voice Guidelines
Write like a knowledgeable friend who happens to be a cardiologist—not like an expert talking down.
DO:
- "Here's what this actually means for you..."
- "The short version is..."
- "Scientists figured out that..."
- "What surprised researchers was..."
- "In plain terms..."
DON'T:
- "It's important to understand that..."
- "One must consider..."
- "The clinical implications are..."
- "Healthcare providers should..."
Handling Trial Names
General rule: If YOU need the trial name to verify the science, keep it in your references. The reader almost never needs it.
When to mention trial names: Only when the name itself is widely known by patients (rare) or when you're writing a longer piece where you'll reference the same trial multiple times.
How to handle:
- ❌ "The SELECT trial showed..."
- ✅ "A large study of people taking semaglutide..."
- ✅ "When researchers tested this in over 17,000 patients..."
- ✅ "The biggest study to date found..." (cite in references)
Handling Statistics
Convert ALL statistics to human terms:
| Academic | For People |
|---|---|
| 35% relative risk reduction | About 1 in 3 fewer events |
| HR 0.74 (95% CI 0.65-0.85) | Roughly a quarter less likely |
| NNT = 25 | For every 25 people treated, 1 person benefits |
| p < 0.001 | Very strong evidence (drop this entirely usually) |
| Median follow-up 4.2 years | After about 4 years |
| Primary composite endpoint | The main things researchers were counting |
When to use numbers:
- Use actual numbers for things people can visualize: "3,000 patients" is fine
- Use fractions/ratios for effects: "about 1 in 10" is better than "10%"
- Use comparisons: "roughly the same risk reduction as stopping smoking"
Word Substitutions
See references/plain-language-guide.md for complete list. Quick reference:
| Medical | Plain |
|---|---|
| myocardial infarction | heart attack |
| cardiovascular death | death from heart problems |
| hospitalization for heart failure | ending up in the hospital because your heart is struggling |
| composite endpoint | combination of outcomes |
| randomized controlled trial | well-designed study where patients were randomly assigned |
| placebo | sugar pill / inactive treatment |
| statistically significant | unlikely to be a coincidence |
| hazard ratio | risk comparison |
| mechanism of action | how the drug works |
| pharmacokinetics | how the drug moves through your body |
| adverse events | side effects |
| contraindicated | shouldn't be used |
| comorbidities | other health conditions |
| titration | adjusting the dose |
| prognosis | likely outcome |
Step 4: Verify Accuracy
Before finalizing, confirm:
- Every factual claim has a PubMed reference you can cite
- Numbers haven't been distorted in translation
- Simplification hasn't created inaccuracy
- A cardiologist reading this would nod, not cringe
Step 5: Add References Section
At the end, include a "Sources" or "The Evidence" section:
Format:
## The Evidence
1. The heart failure drug study mentioned: McMurray JJV et al. Dapagliflozin in Patients with Heart Failure and Reduced Ejection Fraction. N Engl J Med. 2019;381(21):1995-2008. DOI: 10.1056/NEJMoa1911303
2. The weight loss medication study: Lincoff AM et al. Semaglutide and Cardiovascular Outcomes in Obesity without Diabetes. N Engl J Med. 2023;389(24):2221-2232. DOI: 10.1056/NEJMoa2307563This section serves two purposes: 1. Your user can verify you interpreted the science correctly 2. Curious readers can dig deeper
Length Guidelines
Short form (tweets, posts): 280-500 words
- One core message
- One surprising fact or reframe
- One takeaway
Medium form (newsletter, article): 800-1500 words
- Clear story arc
- 2-3 supporting points
- Practical implications
- References at end
Long form (deep dive): 2000-3000 words
- Can handle more complexity
- Still no jargon
- More context and nuance
- Multiple reference sources
Voice Positioning
Write as: An interventional cardiologist who genuinely enjoys explaining medicine to interested laypeople. Not talking down. Not dumbing down. Just... talking.
First person is okay:
- "In my clinic, I see patients who..."
- "What I find remarkable is..."
- "When I explain this to patients, I tell them..."
Reader relationship:
- They're intelligent but not medically trained
- They can handle complexity if it's explained well
- They want to understand, not just be told
- They'll tune out if you sound like a textbook
Workflow Decision Tree
START: User wants to explain cardiology science to general audience
│
├─→ What's the source material?
│ ├─ Trial/Paper → Research via PubMed MCP
│ ├─ Topic/Question → Research via PubMed MCP
│ └─ User-provided PDF/abstract → Extract key points, verify via PubMed
│
├─→ Research phase
│ ├─ Use PubMed:search_articles for relevant trials
│ ├─ Get 5-8 references from major journals
│ ├─ Extract: What actually happened? What does it mean?
│ └─ Identify the human story
│
├─→ Writing phase
│ ├─ Lead with story/meaning, not data
│ ├─ Translate all statistics to human terms
│ ├─ Background trial names (references section)
│ ├─ Foreground understanding and implications
│ ├─ Keep language at 8th grade reading level
│ └─ Maintain full scientific accuracy
│
├─→ Verification phase
│ ├─ Would a cardiologist approve the accuracy?
│ ├─ Would an 8th grader understand it?
│ ├─ Is every claim backed by a citable reference?
│ └─ Have statistics been translated without distortion?
│
└─→ OUTPUT: Rigorous science in human language + reference sectionQuality Checklist
Before delivering:
- [ ] Would someone without medical training understand every sentence?
- [ ] Have all trial acronyms been removed or minimally used?
- [ ] Have all statistics been converted to human terms?
- [ ] Is there a clear "so what" for the reader's life?
- [ ] Does it start with a story or hook, not data?
- [ ] Could a cardiologist verify every claim?
- [ ] Are 5-8 references included at the end?
- [ ] Is the language warm and conversational, not lecturing?
- [ ] Would someone actually want to read this?
- [ ] Is this accurate enough for the user to post without embarrassment?
What This Skill is NOT
- Not dumbed-down medicine ("eat less, move more")
- Not medical advice ("talk to your doctor about...")
- Not press-release hype ("breakthrough", "game-changing")
- Not academic writing made slightly simpler
- Not content requiring another LLM to understand
Example Transformation
Academic (original editorial style): "The SELECT trial (Lincoff et al., NEJM 2023) randomized 17,604 adults with established cardiovascular disease and BMI ≥27 without diabetes to semaglutide 2.4 mg weekly or placebo. The primary endpoint—a composite of cardiovascular death, nonfatal MI, or nonfatal stroke—occurred in 6.5% of the semaglutide group versus 8.0% of the placebo group (HR 0.80, 95% CI 0.72-0.90, p<0.001), representing a 20% relative risk reduction over a median follow-up of 39.8 months."
For People (this skill): "Here's something that surprised even cardiologists: a weight loss drug might actually protect your heart—independent of how much weight you lose.
Researchers studied over 17,000 people who already had heart disease and were overweight or obese. Half got weekly injections of semaglutide (the active ingredient in Ozempic and Wegovy), and half got a placebo. After about three years, those on semaglutide were roughly 20% less likely to have a heart attack, stroke, or die from heart problems.
What's striking is this: the benefit showed up before people lost much weight. Something about the drug itself seems to protect blood vessels. Scientists are still working out exactly how, but the evidence was strong enough that cardiologists are now taking notice—especially for patients who have both heart disease and weight to lose.
The bottom line? If you have heart disease and are considering weight loss medications, this one might do double duty. Worth a conversation with your cardiologist."
Same facts. Different packaging. One requires medical training to appreciate. One doesn't.
Essential References
references/plain-language-guide.md- Complete vocabulary translationsreferences/storytelling-patterns.md- Narrative structures that work
Plain Language Guide
Comprehensive vocabulary translations for converting medical writing to accessible science.
Core Principle
Every medical term exists for precision. But precision between doctors is different from precision for patients. Your job is to preserve the MEANING while changing the WORDS.
Anatomy & Physiology
| Medical | Plain |
|---|---|
| myocardium | heart muscle |
| endothelium | inner lining of blood vessels |
| atherosclerosis | buildup of fatty deposits in arteries |
| stenosis | narrowing |
| occlusion | blockage |
| perfusion | blood flow |
| ischemia | not getting enough blood |
| infarction | death of tissue from lack of blood |
| arrhythmia | irregular heartbeat |
| tachycardia | fast heart rate |
| bradycardia | slow heart rate |
| ejection fraction | how well your heart pumps |
| systolic | when heart squeezes |
| diastolic | when heart relaxes |
| ventricle | main pumping chamber |
| atrium | upper chamber |
| valvular | relating to heart valves |
| cardiomyopathy | disease of the heart muscle |
Conditions
| Medical | Plain |
|---|---|
| myocardial infarction (MI) | heart attack |
| acute coronary syndrome | heart attack or severe warning signs |
| heart failure (HF) | heart not pumping strongly enough |
| HFrEF | heart failure where the heart squeezes weakly |
| HFpEF | heart failure where the heart is stiff |
| atrial fibrillation | irregular, often fast, upper heart rhythm |
| hypertension | high blood pressure |
| hyperlipidemia | high cholesterol |
| peripheral artery disease | blocked arteries in the legs |
| cerebrovascular accident | stroke |
| TIA | mini-stroke / warning stroke |
| aortic stenosis | narrowing of the main heart valve |
| mitral regurgitation | leaky heart valve |
Procedures
| Medical | Plain |
|---|---|
| percutaneous coronary intervention (PCI) | opening blocked arteries with a catheter |
| coronary artery bypass grafting (CABG) | heart bypass surgery |
| transcatheter aortic valve replacement (TAVR) | replacing a heart valve without open surgery |
| ablation | destroying abnormal heart tissue |
| cardiac catheterization | threading a tube to the heart to diagnose problems |
| angiogram | pictures of your arteries |
| angioplasty | opening narrowed arteries with a balloon |
| stent | metal scaffold to hold arteries open |
| cardioversion | shock to restore normal heart rhythm |
| ICD implantation | putting in a device that can shock the heart back to rhythm |
| pacemaker implantation | putting in a device to keep heart rate steady |
Research & Statistics
| Medical | Plain |
|---|---|
| randomized controlled trial (RCT) | well-designed study where patients were randomly assigned |
| placebo | inactive treatment (sugar pill) |
| placebo-controlled | compared against inactive treatment |
| double-blind | neither doctor nor patient knew who got what |
| open-label | everyone knew who got what treatment |
| primary endpoint | main thing researchers were measuring |
| secondary endpoint | other things researchers tracked |
| composite endpoint | combination of outcomes |
| hard endpoints | serious outcomes like death or heart attack |
| surrogate endpoints | measurements that suggest (but don't prove) benefit |
| intention-to-treat | analyzing everyone assigned to a group, even if they stopped treatment |
| per-protocol | analyzing only those who completed treatment as planned |
| subgroup analysis | looking at specific types of patients |
| post-hoc analysis | analysis not planned before the study |
| interim analysis | checking results before study finished |
| non-inferiority trial | study to show new treatment is at least as good |
| superiority trial | study to show new treatment is better |
| crossover | patients switched treatments during study |
Statistical Terms
| Medical | Plain | Example Translation |
|---|---|---|
| hazard ratio (HR) | risk comparison | "HR 0.80 means 20% lower risk" → "about 1 in 5 fewer events" |
| relative risk reduction | proportional decrease | "35% RRR" → "about a third fewer" |
| absolute risk reduction | actual decrease | "3% ARR" → "3 fewer events per 100 people" |
| number needed to treat (NNT) | people to treat for one to benefit | "NNT 25" → "treat 25 people to help 1" |
| number needed to harm (NNH) | people treated for one to be harmed | "NNH 100" → "1 in 100 had a side effect" |
| confidence interval | range of likely true values | "95% CI 0.72-0.90" → "probably between 10% and 28% benefit" |
| p-value | chance result is coincidence | "p<0.001" → "very strong evidence" or just omit |
| statistically significant | unlikely to be random | "the difference was real, not just chance" |
| median | middle value | "about half above, half below" |
| mean | average | "on average" |
| incidence | new cases | "how many new cases appeared" |
| prevalence | total cases | "how many people have it" |
Medications
| Medical | Plain |
|---|---|
| pharmacotherapy | drug treatment |
| mechanism of action | how the drug works |
| pharmacokinetics | how the drug moves through your body |
| half-life | how long the drug stays active |
| titration | adjusting the dose |
| loading dose | higher initial dose |
| maintenance dose | ongoing dose |
| adherence | taking medication as prescribed |
| polypharmacy | taking many medications |
| adverse event | side effect |
| serious adverse event | major side effect |
| contraindicated | shouldn't be used |
| off-label | using drug for something it wasn't approved for |
| first-line therapy | first choice treatment |
| second-line therapy | backup treatment |
Clinical Terms
| Medical | Plain |
|---|---|
| prognosis | likely outcome |
| morbidity | illness, suffering |
| mortality | death |
| all-cause mortality | death from any cause |
| cardiovascular mortality | death from heart or blood vessel problems |
| hospitalization | ending up in the hospital |
| readmission | going back to hospital |
| quality of life | how good you feel day-to-day |
| functional capacity | what you're able to do |
| symptom burden | how much symptoms bother you |
| comorbidities | other health conditions |
| risk stratification | figuring out who's high or low risk |
| guideline-directed | recommended by expert guidelines |
| standard of care | usual treatment |
| clinical equipoise | doctors genuinely uncertain which is better |
Framing Statistics for People
Converting Hazard Ratios
| HR | Meaning | People-friendly phrasing |
|---|---|---|
| 0.50 | 50% lower | "cut the risk in half" |
| 0.65 | 35% lower | "about a third less likely" |
| 0.75 | 25% lower | "roughly a quarter less likely" |
| 0.80 | 20% lower | "about 1 in 5 fewer events" |
| 0.85 | 15% lower | "about 15% less likely" |
| 0.90 | 10% lower | "about 1 in 10 fewer events" |
| 1.00 | no difference | "no real difference" |
| 1.20 | 20% higher | "about 1 in 5 more likely" |
| 1.50 | 50% higher | "about half again as likely" |
| 2.00 | 100% higher | "twice as likely" |
Absolute Risk in Human Terms
Template: "For every [X] people treated for [time], [Y] fewer had [outcome]."
Examples:
- "For every 100 people who took the drug for 3 years, about 3 fewer had heart attacks."
- "If 25 people with heart failure take this medication, about 1 of them will avoid hospitalization."
- "Out of 1,000 people on blood thinners for a year, about 15 will have some bleeding—usually minor."
Comparisons That Help
Use real-world comparisons when possible:
- "Similar benefit to what you'd get from stopping smoking"
- "Roughly the same risk reduction as taking a statin"
- "The risk is about as high as driving a car for a year"
- "Less dangerous than riding a motorcycle"
Phrases to Avoid
| Don't write | Write instead |
|---|---|
| "It is important to note that..." | Just state it |
| "Healthcare providers should consider..." | "Your doctor might consider..." |
| "Patients should be counseled on..." | "This is worth knowing..." |
| "The data suggest..." | "The evidence shows..." or "Studies found..." |
| "In the clinical setting..." | "In practice..." or "When doctors see patients..." |
| "Evidence-based medicine dictates..." | "Based on the research..." |
| "The literature demonstrates..." | "Studies show..." |
| "Per clinical guidelines..." | "Expert recommendations suggest..." |
Trial Name Handling
When you must mention a trial
Keep it minimal and parenthetical:
- "A major study called DAPA-HF found..."
- "The largest trial to date (called SELECT) showed..."
- "In a study that followed patients for years..."
When to skip trial names entirely
Most of the time. Just describe:
- "When researchers tested this in 17,000 patients..."
- "A well-designed study of people with heart failure..."
- "Multiple studies have now confirmed..."
Acronym expansion
If you must use an acronym, never expect readers to remember it:
- ❌ "The PARADIGM-HF trial showed... Later, PARADIGM-HF also demonstrated..."
- ✅ "A major heart failure study showed... That same research also demonstrated..."
Storytelling Patterns for Science Communication
Structures and techniques for turning clinical trials into stories people want to read.
Core Truth
People don't remember statistics. They remember stories. Your job is to wrap accurate science in memorable narrative.
The Five Opening Hooks
1. The Surprising Reversal
Start with something that contradicts expectations.
Template: "[Thing people assume] might actually be wrong. Here's what we're learning..."
Examples:
- "For years, we told people with heart disease to avoid fats. Turns out, the type of fat matters far more than we thought."
- "A diabetes drug is now one of the best heart failure treatments we have—even for people without diabetes."
- "Doctors used to believe that fixing blocked arteries always helped patients live longer. The data tell a different story."
Why it works: Cognitive dissonance. Readers need to resolve the surprise.
2. The Human Moment
Start with a relatable patient scenario.
Template: "Imagine you're [situation]. [Problem]. [Question this raises]..."
Examples:
- "Imagine you're 68, just had a stent placed in your heart, and you're wondering how long you'll need to take blood thinners. It's a question your doctor asks herself too."
- "You're a weekend warrior who suddenly gets chest pain during a basketball game. Heart attack? Panic attack? Here's how doctors figure it out."
- "Your mom just had her second bout of heart failure. You're wondering if this is the new normal. There's reason for hope."
Why it works: Personal stakes. Reader sees themselves.
3. The Mystery Opening
Frame the science as a puzzle being solved.
Template: "Scientists noticed something strange: [observation]. They set out to understand why."
Examples:
- "Researchers noticed that people taking a certain diabetes medication seemed to have fewer heart attacks. It wasn't supposed to work that way. So they designed a study to find out what was happening."
- "For decades, cardiologists puzzled over why some people with blocked arteries never had heart attacks while others with clear arteries did."
Why it works: Curiosity. Readers want to solve the puzzle too.
4. The "What We Got Wrong" Opening
Admit past mistakes or limitations.
Template: "For years, we believed [old thinking]. New evidence is changing that."
Examples:
- "For a long time, doctors thought the best way to help heart failure was to make the heart squeeze harder. We were focused on the wrong problem."
- "Cardiologists used to think diet didn't matter much compared to medications. We underestimated food."
Why it works: Humility builds trust. Shows science evolves.
5. The Consequence Hook
Start with what's at stake.
Template: "[Big number] people face [problem]. [What's changing]..."
Examples:
- "About 6 million Americans live with heart failure. Until recently, our medications were frustratingly limited. That's finally changing."
- "Every year, 800,000 people in the US have a stroke. A surprising number could have been prevented—if we'd caught a hidden heart problem."
Why it works: Scope. Readers understand this matters.
Narrative Structures
Structure 1: Problem → Discovery → Meaning
The classic science story arc.
1. Establish the problem (1-2 paragraphs)
- What was the situation before?
- Why was it frustrating/dangerous/limited?
- What were people doing about it?
2. Introduce the discovery (2-3 paragraphs)
- What did researchers do?
- What did they find?
- Why is it surprising or important?
3. Land the meaning (1-2 paragraphs)
- What changes now?
- What does this mean for readers?
- What questions remain?
Example skeleton:
For years, people with [condition] had limited options. [Old approach] helped some, but [limitation]. [Why this mattered].
>
Then researchers tried something different. In a study of [number] people, they tested [intervention]. After [time], those getting [treatment] were [result]. Even more striking, [secondary finding].
>
What does this mean for you? If you have [condition], [practical implication]. The evidence is strong enough that [current status]. Worth asking your doctor about.
Structure 2: Old Belief → Evidence → New Understanding
For pieces that challenge conventional wisdom.
1. State the old belief (1 paragraph)
- What did we used to think?
- Why did it seem reasonable?
2. Present the contradicting evidence (2-3 paragraphs)
- What studies challenged this?
- What did they show?
- How solid is the evidence?
3. Synthesize new understanding (1-2 paragraphs)
- What do we think now?
- What's the nuance?
- What remains uncertain?
Structure 3: Question → Investigation → Answer (Sort Of)
For topics where the answer is nuanced.
1. Pose the question readers actually have (1 paragraph)
- Frame it the way a patient would ask
- Acknowledge why it matters
2. Walk through the evidence (2-3 paragraphs)
- What have studies shown?
- What are the competing findings?
- What explains the differences?
3. Give the best current answer (1-2 paragraphs)
- What's the most accurate summary?
- For whom does this apply?
- What caveats matter?
Structure 4: Person → Problem → Progress
Character-driven narrative (composite patients).
1. Introduce someone relatable (1 paragraph)
- A composite patient representing the population
- Their specific challenge
- (Note: Always make clear this is representative, not a specific individual)
2. Explain their problem in scientific terms (1-2 paragraphs)
- What's happening in their body?
- What were the options?
3. Show what's changed (1-2 paragraphs)
- What new options exist?
- How does the evidence apply to them?
- What's the realistic outlook?
Transitions That Work
Moving from Story to Science
- "Here's what's actually happening inside..."
- "The science behind this is..."
- "To understand why, we need to look at..."
- "Researchers set out to test this..."
Moving from Science to Implications
- "In practical terms, this means..."
- "For you, this translates to..."
- "The bottom line for patients is..."
- "What this means for your daily life..."
Moving from Evidence to Uncertainty
- "What we still don't know is..."
- "The evidence is strongest for... but less clear for..."
- "This works well for [group], but we need more research on..."
- "One thing to keep in mind..."
Ending Strong
The Takeaway Close
Summarize the single most important point.
Template: "The bottom line: [core message]. [Practical application]."
Example: "The bottom line: your cholesterol numbers matter less than the type of cholesterol and what you do about it. If your doctor mentions LDL, it's worth having a real conversation about your options."
The Forward-Looking Close
Point to what's coming.
Template: "This is just the beginning. [What's next]. [Why that matters]."
Example: "This is just the beginning. Researchers are now testing whether starting treatment earlier could prevent heart failure from developing in the first place. In a few years, we might be having a very different conversation."
The Empowerment Close
Give readers something to do.
Template: "If [you fit this profile], [specific action]. [Why it's worth it]."
Example: "If you have diabetes and are already on a GLP-1 medication, ask your cardiologist whether your heart might be benefiting too. It's a conversation worth having."
The Perspective Close
Zoom out to larger context.
Template: "[What we covered] is part of [bigger picture]. [What this signals]."
Example: "This new approach to heart failure reflects something bigger happening in medicine: we're learning to treat the whole system, not just one organ. Your heart, kidneys, and metabolism are more connected than we realized—and that's good news for treatment."
Things to Avoid
Don't End With Uncertainty
- ❌ "More research is needed."
- ❌ "The picture isn't clear yet."
- ❌ "Time will tell."
(These are true but unsatisfying. If you must acknowledge uncertainty, pair it with something actionable.)
Don't End With a Disclaimer
- ❌ "Always consult your doctor before making changes."
- ❌ "This is not medical advice."
(If needed, put this earlier. Don't make it your last impression.)
Don't Summarize the Summary
- ❌ "In conclusion, we've seen that [repeats everything]."
(Trust your reader. They got it.)
Tone Calibration
Match the subject
- Exciting discovery: Enthusiasm is okay, but measured ("This is genuinely exciting because...")
- Nuanced findings: Thoughtful, balanced ("The answer is more complicated, but that's actually good news...")
- Overturned belief: Humble, exploratory ("We got this wrong, and here's what we're learning...")
- Practical guidance: Direct, confident ("Here's what the evidence supports...")
Avoid academic hedge-stacking
- ❌ "It may potentially be possible that some patients might consider..."
- ✅ "For some patients, this could help."
Show your humanity
Occasional personal observations build trust:
- "This surprised me when I first read the data."
- "I find this both encouraging and a little humbling."
- "What struck me most was..."