The hard art of soft science: Evidence‐Based Medicine, Reasoned Medicine or both?
Bibliographic record
Abstract
In the past 14 years, Evidence-Based Medicine (EBM) has enjoyed unprecedented developments and gained widespread acceptance among health professionals. However, should we be content with producing, critically appraising and using the best evidence available for our understanding of health problems and decision making about them? Are our convictions about EBM's relevance, our conviction and intellectual satisfaction with its mastery and adoption enough? Should we continue pushing forward along this promising path, or should we further diversify the content and scope of EBM? Is EBM the only way to view medicine in the near future? This paper presents some options to choose from in terms of direction and content as well as questions to answer given the current EBM crossroads. More intensive and extensive EBM combined with 'other features'-based medicines may be the preferred strategy to follow in the future to determine the development, use and evaluation of EBM. Argument-based medicine or Reasoned Medicine is one of the options that can be integrated into the mainstream of medical reasoning and decision making.
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How this classification was reachedexpand
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 | 0.058 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.005 | 0.093 |
| Scholarly communication | 0.030 | 0.043 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.016 | 0.020 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".