Dr. Roberts et al reply
Bibliographic record
Abstract
To the Editor: We thank Dr. Steiman and colleagues1 for their interest and correspondence regarding our recent publication exploring the feasibility of physiotherapy-led rheumatology triage using a standardized triage algorithm.2 The algorithm used in this study relied on the referring providers’ physical examination, laboratory results, and radiographic results, as well as patient-reported questionnaire scores. Those referrals that reached a prespecified threshold score based on this algorithm, without in-person assessment, were triaged as urgent and assessed in clinic by a rheumatologist. This differs from the face-to-face triage performed by extended role practitioners (ERPs) in several other studies that have … Address correspondence to Dr. J.H. Roberts, Division of Rheumatology and Department of Medicine, Queen Elizabeth II Health Sciences Center and Dalhousie University, Nova Scotia Rehabilitation and Arthritis Centre, 1341 Summer Street, Halifax, NS B3H 4K4, Canada. Email: janet3.roberts{at}nshealth.ca.
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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.033 | 0.032 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".