A well-conducted randomized trial that establishes no benefit of therapy is an important medical advance
Bibliographic record
Abstract
As physicians, we continually strive to understand disease processes and predict how they will affect our patients. However, our greatest skill lies in our ability to intervene and change the natural history of a disease, i.e. change a poor outcome which otherwise would have occurred. Many types of interventions are used in the care of renal patients, from pills, to procedures and dialysis, to alternative ways to deliver health care. All would agree that interventions need to be evaluated to determine if they are beneficial, without harm, and cost-effective in a system of finite resources. A randomized, controlled clinical trial (RCT) is an experimental method used to evaluate the effectiveness of an intervention. RCTs are conducted when an intervention shows the potential for health care improvement, but there is collective uncertainty as to the true benefits of the intervention [1]. This uncertainty is in fact essential to the clinical trial paradigm, as otherwise clinical equipoise would be violated, and in most cases it would not be ethical to randomly assign patients between the intervention to be tested and a control intervention. This is true both when the control treatment is a placebo and when the control entails administration of an active treatment reflecting standard therapy [2]. A logical consequence of the uncertainty required to maintain equipoise is that a substantial proportion, in fact the majority, of well-conducted RCTs must be negative.
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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 | 0.033 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".