Elevating the quality of disability and rehabilitation research
Bibliographic record
Abstract
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 (Physical Therapy, the Journal of Orthopaedic & Sports Physical Therapy, the Journal of Physiotherapy, and the European Journal of Physical and Rehabilitation Medicine have already successfully required reporting guidelines, one for as many as 10 years).Research reportsmust 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 (Moher, Simera, Schulz, Hoey, & Altman, 2008). By January 1, 2015, all of the journals publishing this editorial will have worked through implementation and the mandatory use of guidelines, and checklists will be firmly in place. Because each journal has its unique system for managing submissions, there may be several ways that these reporting requirements will be integrated into the manuscript flow.
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 | 0.066 | 0.249 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.028 | 0.012 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.019 | 0.030 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".