CONSORT 2025 explanation and elaboration: updated guideline for reporting randomised trials
Bibliographic record
Abstract
This is comment on: Hopewell S, et al. CONSORT 2025 explanation and elaboration: updated guideline for reporting randomised trials. BMJ. 2025 Apr 14;389:e081124. https://pubmed.ncbi.nlm.nih.gov/40228832 The CONSORT (2025) statement [1] writes about blinding as follows: “Unblinded outcome assessors may differentially assess subjective outcomes, and unblinded data analysts may introduce bias through the choice of analytical strategies, such as the selection of favourable time points or outcomes and by decisions to remove patients from the analyses. These biases have been well documented,” p.27 in [1]. One of the references to this statement, ref. no. 328, is to the trial by Karlowski, Chalmers et al. (1975) [2]. In the randomized, double-blind, placebo-controlled trial, Thomas Karlowski, Thomas Chalmers et al. observed that 6 g/day vitamin C significantly shortened the duration of colds, yet they concluded “that the effects demonstrated might be explained equally well by a break in the double blind.” The Karlowski (1975) trial has been widely used as evidence for the existence of the placebo effect, and also as evidence that the observed effects of vitamin C on the common cold are explained by the placebo effect [3]. However, it was shown already in 1996 that the placebo-effect interpretation of Karlowski, Chalmers, et al. was not valid [4-6].
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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 | 0.185 | 0.548 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.010 | 0.015 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.011 | 0.007 |
| Research integrity | 0.021 | 0.026 |
| Insufficient payload (model declined to judge) | 0.125 | 0.086 |
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".