CONSORT Statement for Randomized Trials of Nonpharmacologic Treatments: A 2017 Update and a CONSORT Extension for Nonpharmacologic Trial Abstracts
Bibliographic record
Abstract
Incomplete and inadequate reporting is an avoidable waste that reduces the usefulness of research. The CONSORT (Consolidated Standards of Reporting Trials) Statement is an evidence-based reporting guideline that aims to improve research transparency and reduce waste. In 2008, the CONSORT Group developed an extension to the original statement that addressed methodological issues specific to trials of nonpharmacologic treatments (NPTs), such as surgery, rehabilitation, or psychotherapy. This article describes an update of that extension and presents an extension for reporting abstracts of NPT trials. To develop these materials, the authors reviewed pertinent literature published up to July 2016; surveyed authors of NPT trials; and conducted a consensus meeting with editors, trialists, and methodologists. Changes to the CONSORT Statement extension for NPT trials include wording modifications to improve readers' understanding and the addition of 3 new items. These items address whether and how adherence of participants to interventions is assessed or enhanced, description of attempts to limit bias if blinding is not possible, and specification of the delay between randomization and initiation of the intervention. The CONSORT extension for abstracts of NPT trials includes 2 new items that were not specified in the original CONSORT Statement for abstracts. The first addresses reporting of eligibility criteria for centers where the intervention is performed and for care providers. The second addresses reporting of important changes to the intervention versus what was planned. Both the updated CONSORT extension for NPT trials and the CONSORT extension for NPT trial abstracts should help authors, editors, and peer reviewers improve the transparency of NPT trial reports.
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 | 0.416 | 0.670 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.016 | 0.023 |
| Bibliometrics | 0.020 | 0.031 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.012 | 0.013 |
| Research integrity | 0.012 | 0.033 |
| Insufficient payload (model declined to judge) | 0.035 | 0.023 |
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".