Clinical Trial Registration in Physical Therapy Journals: Recommendations from the International Society of Physiotherapy Journal Editors
Bibliographic record
Abstract
Clinical trial registration involves placing the protocol for a clinical trial on a free, publicly available, and electronically searchable register. Registration is considered to be prospective if the protocol is registered before the trial commences (ie, before the first participant is enrolled). Prospective registration has several potential advantages. It could help avoid trials being duplicated unnecessarily, and it could allow people with health problems to identify trials in which they might participate. Perhaps more important, however, it tackles 2 big problems in clinical research: selective reporting and publication bias. Selective reporting involves investigators reporting only the most favorable results when they publish a trial, instead of reporting the results for all the outcomes that were measured. Reporting only favorable outcomes can create a misleading appearance of the effect of a therapy in the published literature. For example, imagine that a completely ineffective intervention is tested across several trials, and each trial measures multiple outcomes. Most outcomes will show no significant effect of the intervention. However, occasionally an outcome will show significant benefit or harm simply by chance. If the researchers publish the positive outcomes but not all of the nonsignificant and negative outcomes, readers could interpret falsely that the intervention is beneficial. A similar problem could occur when outcomes are analyzed at multiple time points. Researchers may report that an intervention improves walking speed at 6 months, but fail to mention that it does not improve walking speed at 1, 2, 3, 9, 12, and 24 months. Prospective registration of clinical trials combats this problem in several ways. Journal editors and reviewers can compare the range of outcomes reported in a manuscript against those listed in the registered protocol, requesting that any discrepancies be resolved by following the protocol. Readers also can compare the outcomes in the registered protocol against …
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.063 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.006 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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; both teacher heads 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".