Bibliographic record
Abstract
When may a physician legitimately offer enrollment in a randomized clinical trial (RCT) to her patient? Two answers to this question have had a profound impact on the research ethics literature. Equipoise, as originated by Charles Fried, which we term Fried's equipoise (FE), stipulates that a physician may offer trial enrollment to her patient only when the physician is genuinely uncertain as to the preferred treatment. Clinical equipoise (CE), originated by Benjamin Freedman, requires that there exist a state of honest, professional disagreement in the community of expert practitioners as to the preferred treatment. FE and CE are widely understood as competing concepts. We argue that FE and CE offer separable and, in themselves, incomplete justifications for the conduct of clinical trials. FE articulates conditions under which the fiduciary duties of physician to patient may be upheld in the conduct of research. CE sets out a standard for the social approval of research by institutional review boards. Viewed this way, FE and CE are not necessarily competing notions, but rather address complementary moral concerns.
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 | 0.187 | 0.308 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.104 |
| Scholarly communication | 0.016 | 0.027 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.039 | 0.043 |
| 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".