For and against: Clinical equipoise and not the uncertainty principle is the moral underpinning of the randomised controlled trial FOR AGAINST
Bibliographic record
Abstract
# Clinical equipoise and not the uncertainty principle is the moral underpinning of the randomised controlled trial {#article-title-2} The ethical basis for entering patients in randomised controlled trials is under debate. Some doctors espouse the uncertainty principle whereby randomisation to treatment is acceptable when an individual doctor is genuinely unsure which treatment is best for a patient. Others believe that clinical equipoise, reflecting collective professional uncertainty over treatment, is the soundest ethical criterion. Here doctors from two Canadian centres discuss their positions. # For {#article-title-3} On what ethical grounds may a physician offer trial participation to his or her patient? The answer seems to depend greatly on which side of the Atlantic you reside. In the United Kingdom, the uncertainty principle is widely endorsed. 1 2 However, in North America, clinical equipoise—reflecting collective uncertainty—is the dominant ethical basis.3 Which of these principles offers the preferred moral underpinning for the randomised controlled trial? It is widely acknowledged that physicians have a primary duty to promote their patients' welfare. When physicians become investigators, however, other ends such as recruiting enough subjects and retaining them in the trial may conflict with this duty.4 How can the physician maintain fidelity to the patient and further the ends of a randomised controlled trial? The uncertainty principle offers an appealing solution to this problem. Physicians who are convinced that one treatment is better than another for a particular patient cannot ethically choose at random which treatment to give, they must do what they think best for the patient. For this reason, physicians who feel they already know the answer cannot enter their patients into a trial. If they think, whether for a wise or silly reason, that they know the answer before the trial starts, they should not enter any patients.2 On the other hand, if the physician is uncertain about which treatment is best for a patient, offering the patient randomisation to equally …
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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 | 0.198 | 0.423 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.055 |
| Scholarly communication | 0.020 | 0.022 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.038 | 0.042 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".