One of the proposed solutions of the EBM Manifesto Educate the public in evidence-based healthcare to make informed decisions
Bibliographic record
Abstract
Evidence-based healthcare, shared decision making, minimally disruptive medicine and value-based healthcare are all different tools for shaping the future of healthcare. They represent different ways of addressing various problems many countries are facing in providing more value for patients, improving health and reducing sickness. As a physician, apart from taking care of patients, I teach the use of evidence in practice, to both students and colleagues. In this text though, I want to relate my experience about something different, that is, educating the lay public. For many years now, I have had the opportunity to address groups of men and women aged from 30 to 60 years attending preretirement seminars. In that setting, I taught more than 1500 individuals. The themes I cover are diverse but include life habits and their impact on good health, addressing risks, how screening is a choice and questions they should ask their providers when offered different options in addressing their health issues. I describe some of the lessons I have learnt throughout these years. Some of the conclusions I make have not been formally studied but I share my experience hoping to foster thoughts on how to best address this objective of the EBM manifesto. ### First lesson Patients are very aware of their responsibility to take control of their life. It might not seem like it in our offices when we see patients one on …
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.125 | 0.149 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.033 |
| Scholarly communication | 0.022 | 0.030 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.032 | 0.044 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".