Turn a headline into a question you can check.
Start with the comparison, name the outcome and put the numbers in context. Then connect the finding to other research without losing the uncertainty along the way.
AI-written reading guide · Source notes & coverage
Choose a reading question
Choose the part of a paper that is holding you up. Each question leads to a worked guide and a glossary entry.
Find the design, groups and assignment method before reading a causal claim.
Write the comparison as “this group versus that group”. If you cannot name both, return to the methods before repeating the result.
Look for the denominator, effect scale, comparison-group risk and time frame.
Keep a four-part note: outcome, number affected, number observed and duration. An empty field is a question to resolve, not a detail to guess.
Read the confidence interval in relation to the actual question and any pre-specified decision boundary.
When a paper asks whether an approach is non-inferior, copy the margin and its scale into your notes. A result near zero alone does not answer that question.
Check whether the wider review is transparent about its question, search, included studies and synthesis.
Put two studies side by side before declaring a contradiction. Different populations, outcomes or follow-up may mean that they answered different questions.
3 published reading guides · About 19 minutes · Each can be read on its own.
Begin with a short numerical example: a relative change needs a starting risk.
Read the guideAsk whether the design supports the cause-and-effect wording in the headline.
Read the guideFollow the evidence beyond one paper and examine how the synthesis was assembled.
Read the guideFor a longer reading session: Read a medical headline with confidence
Use the editable study notebook
Use one column in your own notes for each paper. Record what is reported; leave an explicit question where information is missing.
On a narrow screen, scroll the table sideways to read every column.
| Keep track of | Record from the paper | Then ask |
|---|---|---|
| Question | Population, intervention or exposure, comparator and outcome | Does the headline preserve this question? |
| Design | Assignment, measurement and follow-up | What alternative explanations remain? |
| Result | Effect estimate, denominator and time frame | Can the result be expressed in absolute terms? |
| Uncertainty | Confidence interval and important missing information | Which plausible possibilities are still open? |
| Context | Relevant related studies and reasons for differences | What would genuinely change the interpretation? |
Original reading prompts. This is not a validated scoring tool, a systematic review or a treatment recommendation.
These are questions to take back to a paper or a future study. They do not imply that any particular intervention works.
Downstream RNA can be a target for antisense oligonucleotides. Our guide examines a potential source of unintended effects and the evidence needed to explain a fall in gene output.
Simulations, NMR and biochemical experiments probe a transient regulatory pocket. Our guide keeps a predicted movement, an observed structural response and a cleavage measurement separate.
CROSS replaces the mouse X chromosome with a rat counterpart. The useful lesson is the difference between keeping DNA intact, regulating it correctly and supporting development.
A new single-cell method connects a genetic change with the RNA response around it. Our reading guide examines coverage, comparison labels and the gap between a functional score and a diagnosis.
A genetic label follows an activated oocyte cohort through time. We unpack persistence, developmental competence and the denominator behind a striking ovarian finding.
A time-course study links PLPP2 with experimental liver repair. We connect animal perturbations with human biopsy observations while keeping their causal roles distinct.
An ecological approach estimates whether sequencing will reveal more cellular clusters. Our guide separates a useful stopping rule from a claim that a tissue is fully understood.
A new laboratory study connects viral proteins with IAPP aggregation and insulin-cell readouts. We separate binding, cell metabolism, secretion and human-risk questions.
New structures and functional experiments illuminate LPS transport. We explain what makes a mechanism persuasive and what an antibiotic-development claim would still need.
Today’s microbiome study connects shared species with genetic separation. Our guide shows why people, samples, species and inferred history must stay distinct.
A liver-guideline proof of concept reports concordance across clinical scenarios. Our methods guide separates benchmark agreement, agent consensus and evidence of real-world benefit.
A new study reveals stages of spliceosome termination. Our explainer connects structural snapshots, RNA evidence and the question of how cellular machinery becomes ready for reuse.
A mouse study changes one immune signal and gets different answers from different endpoints. The important question is what “stronger immunity” would have to mean.
A new study connects DUSP4 with thermogenic gene activity. We follow the logic from removing a gene to restoring a local response, and ask what each experiment can establish.
New reference curves bring pain reports into a common framework. We explain how to read the population patterns without turning them into a forecast for one person.
A new medical AI paper makes the case for specialized, adaptable models. Our reading guide follows one error measure and asks what a useful deployment test would add.
Genome-based prediction offers a way to shortlist bacteriophages for laboratory testing. The crucial distinction is between a useful match and a proven treatment.
A cross-species analysis offers a common description of changing brain dynamics. It also shows why a shared pattern is only one part of a mechanism.
Connect molecular neighborhoods, bacterial imaging, tissue atlases and cell dynamics with a practical method for turning a map into a testable explanation.
A feedback loop links membrane organization to cell polarity. Reading the mechanism means following the perturbations as well as the images.
Microscopy and chemical tracking connect a lipid with stages of bacterial development. Seeing it there is the beginning of an explanation, not the end.
A new intestinal atlas connects genetic signals to candidate regulatory regions. It helps refine biological questions about Crohn’s disease without turning an association into a treatment promise.
DNA-built compartments reveal how environment reshapes protein condensation. The lesson is to test what a simplified experiment leaves out.
A dual neural organoid brings peripheral and central development into the same experimental setting. Its usefulness depends on the question it can faithfully represent.
A spatial cell atlas links epithelial states with their microbial and cellular neighbors. The most useful reading follows the map and then tests the proposed connections.
A new human brain atlas offers a closer view of ageing. Its real value lies in the questions it makes possible, rather than a personal biological-age score.
New recordings explore silent speech rehearsal. The useful question is how a controlled syllable task might become a reliable communication tool.
Connect organoids, synthetic compartments and neural experiments with a practical guide to what a model preserves, leaves out and can predict.
A fruit-fly experiment raises a precise question about ageing: which part of a nutrient response matters? The answer needs to be separated from the much broader promise of living longer.
A molecular map asks what familiar anatomical boundaries leave out. Here is how to read its colourful regions without mistaking a new classification for a clinical breakthrough.
A new laboratory study examines how a modified RNA base can affect protein translation—and how sequence design can reduce a particular reading error.
An engineered sugar-bearing gel inhibits bacterial growth and biofilms in laboratory tests. Its promise depends on more than looking or behaving like natural mucus.
PRADA tags nearby molecules and adds a way to explore RNA structure. Its value depends on separating proximity, molecular identity and biological function.
A September review asks who produces the evidence behind digital health tools. For readers, the practical task is to inspect both the result and the route that produced it.
A new study follows research published after the original trial report. It raises a useful distinction: more papers, new investigators and new participants are three different things.
A new imaging method follows activity across transparent fish. The promise is a richer view of physiology; the challenge is proving which connections cause what.
An experiment with medical students separates a convincing explanation from evidence that the answer is right.
A randomized study tested the missing link between an AI answer and a person’s decision. Strong model scores did not translate into better choices.
A benchmark experiment shows why a picture-plus-text score needs a closer look at the words beside the image.
Learn to separate adverse events from adverse reactions, count people rather than episodes and interpret zero events without claiming zero risk.
The COMPare project followed trial publications back to their plans. Use its findings to inspect an outcome change without jumping to accusations.
A combined outcome can tell a useful story while leaving its most important chapter unresolved. Work through a first-event example and learn what to look for in the component results.
A worked guide to precision, clinical importance and the difference between an uncertain result and evidence of little effect.
Use two fictional populations to see how sensitivity, specificity and the frequency of a condition shape what a positive result means.
Follow a simple time-to-event model to see why hazard ratios, risks at a fixed time and survival duration answer different questions.
A practical guide to intention-to-treat analysis, per-protocol comparisons and the research question hidden behind an estimand.
Work through absolute risk, relative change, time frames and treatment burden using clearly labelled examples you can check yourself.
Work through a study where the apparent advantage changes under different assumptions about participants whose outcomes were not observed.
“Not unacceptably worse” is a specific statistical question. Four invented confidence intervals show why it is different from equal, better, or simply not statistically different.
See how multiple testing changes a headline, what a p-value does not measure and how a planned analysis differs from an exploratory finding.
Understand questionnaire scores, meaningful change and why a group average can hide very different experiences among participants.
Follow an invented trial from enrolment to outcome assessment, and learn why a random sequence, concealed allocation and blinding solve different problems.
Understand lead-time bias, length bias and overdiagnosis before interpreting a screening headline—and see which outcomes can answer a different question.
Learn to separate a real difference in effects from two different significance labels—and to check the scale on which a difference is claimed.
Build a simple document trail from registration to publication, and learn how to investigate changed outcomes without assuming every amendment is misconduct.
A real research case study on press releases, news coverage and the words that turn an association into a promise.
A historical audit of large trials shows why a literature search needs a results registry—and why an absent paper is not the same as an absent result.
A practical method for connecting studies without counting every paper as a separate vote or mistaking a new result for the final answer.
A laboratory result can change before anyone knows whether patients benefit. Learn how to read surrogate endpoints, validation claims and the outcomes still missing.
Reading questions and prompts prepared and source-checked by AI. No human editorial or clinical review has been completed. The linked guides include their own sources and access notes.
This is a selected reading guide, not a comprehensive evidence search or clinical guideline. Check each article’s scope before applying its conclusions to another question.
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Selected online sections on populations, comparisons, outcomes and time points checked. Supports the reading framework; no treatment effects were extracted. · Accessed 27 Sep 2026
Selected online sections on absolute and relative effects, uncertainty and interpretation checked. The prompts here are original editorial exercises. · Accessed 27 Sep 2026
Accessible full-text reporting statement checked during preparation of the linked meta-analysis guide on 27 September 2026. Reporting transparency is distinct from methodological quality. · Accessed 27 Sep 2026
Final guidance PDF checked during preparation of the linked non-inferiority guide on 27 September 2026. The reading prompt concerns pre-specified margins, not any treatment recommendation. · Accessed 27 Sep 2026