Bibliographic record
Abstract
Evidence-based medicine (EBM) is frequently portrayed as a value-free approach to knowing what kinds of treatment 'really work.' Since practitioners should help patients to improve their health, and EBM tells us which interventions will work, then it follows that we must practice EBM, offering only those interventions supported by evidence. The primary goal of EBM, then, is an ethical one - to improve health. More recently, EBM's authors have also committed themselves to 'shared decision making' in which evidence plays a role in the clinical encounter, but where patients, motivated by their own values, should have final decision-making authority. Envisioned this way, strengthening the informed consent process, rather than improved health per se, is viewed as the goal of EBM. In this paper, I will explore this shift in EBM's ethics from the goal of improved health towards the goal of strengthened informed consent. Drawing upon data from a qualitative enquiry of scholars involved in the development of EBM, I will argue that EBM is now committed to both of these ethical goals. Where they conflict, the aim of the intervention will determine which goal practitioners should pursue. Having increased the ethical complexity of EBM, we are left with the question of whether EBM would still be judged a success if it did not lead to much in the way of improvements in health, but primarily strengthened informed consent. This paper will conclude by arguing that this more nuanced version of EBM's ethics accurately reflects the dynamics of real clinical practice but undermines the original, perceived need for EBM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.233 | 0.232 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.151 |
| Scholarly communication | 0.021 | 0.036 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.022 | 0.033 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".