What sort of bioethical values are the evidence-based medicine and the GRADE approaches willing to deal with?
Bibliographic record
Abstract
The concept of evidence-based medicine (EBM) has been invented by physicians mostly from English Canada, mostly from McMaster University, Ontario, Canada. The term EBM first appeared in the biomedical literature in 1991 in an article written by a prominent member of this group-Gordon Guyatt from McMaster University. The inventors of EBM have also created the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) working group, which is a prominent international organisation whose main purpose is to develop evidence-based clinical practice guidelines (CPGs). CPGs that are based on the GRADE approach are becoming increasingly adopted worldwide, in particular by many professional or governmental organisations. This group of thinkers being thus identified, we have retrieved and read many of their publications in order to try and understand how they intend to incorporate bioethical values into their concept. The author of this little essay did also spend a few years on the internet as an active member of the GRADE group discussion list. The observations thus gathered suggest that although some of the inventors of EBM, at least Gordon Guyatt, wish to incorporate core principles of biomedical ethics into their concept (ie, non-malevolence, beneficence and maybe to a lesser extent respect for autonomy, and justice), some clarifications are still necessary in order to better understand how they intend to more explicitly incorporate bioethical values into their concept and, perhaps more importantly, into evidence-based CPGs.
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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 | 0.138 | 0.449 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.006 | 0.054 |
| Scholarly communication | 0.038 | 0.058 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.021 | 0.026 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".