Bibliographic record
Abstract
From the College of Health and Human Services, The Center for Study of Chronic Illness and Disability, George Mason University, Fairfax, Virginia (LNG); and Department of Medicine, Physical Medicine and Rehabilitation, University of Toronto Scientist, Toronto Rehabilitation Institute, Toronto, Ontario, Canada (DK). All correspondence and requests for reprints should be addressed to: Dinesh Kumbhare, MD, MSc, FRCPC, FAAPMR, Department of Medicine, Physical Medicine and Rehabilitation, University of Toronto Scientist, Toronto Rehabilitation Institute, Toronto, Ontario, Canada. Second Order Peer Review of: Moraska AF, Stenerson L, Butryn N, et al. Myofascial trigger point-focused head and neck massage for recurrent tension-type headache: a randomized, placebo-controlled clinical trial. Clin J Pain. 2015 Feb;31(2):159–68. We did not receive any funding for the preparation of this manuscript. Financial disclosure statements have been obtained, and no conflicts of interest have been reported by the authors or by any individuals in control of the content of this article.
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 | 0.097 | 0.445 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.017 | 0.011 |
| Insufficient payload (model declined to judge) | 0.169 | 0.119 |
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".