Elevating the Quality of Disability and Rehabilitation Research: Mandatory Use of the Reporting Guidelines
Bibliographic record
Abstract
With the remarkable growth of disability-and rehabilitation-related research in the last decade, it is imperative that we support the highest quality research possible.With cuts in research funding, rehabilitation research is now under a microscope like never before, and it is critical that we put our best foot forward.To ensure the quality of the disability and rehabilitation research that is published, the 28 rehabilitation journals simultaneously publishing this editorial (see acknowledgments) have agreed to take a more aggressive stance on the use of reporting guidelines.*Research reports must contain sufficient information to allow readers to understand how a study was designed and conducted, including variable definitions, instruments and other measures, and analytical techniques [1].For review articles, systematic or narrative, readers should be informed of the rationale and details behind the literature search strategy.Too often articles fail to include their standard for inclusion and their criteria for evaluating quality of the studies [2].As noted by Doug Altman, co-originator of the Consolidated Standards of Reporting Trials (CONSORT) statement and head of the Centre for Statistics in Medicine at Oxford University: "Good reporting is not an optional extra: it is an essential component of good research.weall share this obligation and responsibility.[3]"
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 | 0.847 | 0.935 |
| Meta-epidemiology (narrow) | 0.005 | 0.011 |
| Meta-epidemiology (broad) | 0.017 | 0.013 |
| Bibliometrics | 0.024 | 0.026 |
| Science and technology studies | 0.010 | 0.026 |
| Scholarly communication | 0.036 | 0.031 |
| Open science | 0.015 | 0.018 |
| Research integrity | 0.038 | 0.043 |
| Insufficient payload (model declined to judge) | 0.005 | 0.008 |
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".