Digital health tools used to triage musculoskeletal pain in primary, urgent and emergency settings: a scoping review
Bibliographic record
Abstract
We aim to identify digital health tools that can triage musculoskeletal pain in adults across primary, urgent, or emergency care settings. Musculoskeletal conditions are one of the largest contributors to the global burden of disease. Digital health tools can support triage of non-emergent pain, such as musculoskeletal pain, and help patients and clinicians direct the right care at the right time. The digital health research field is growing rapidly, and a summary of digital tools to triage musculoskeletal pain is needed. This knowledge will guide efforts to help patients and clinicians navigate the health system and potentially decrease the burden of musculoskeletal pain in emergency departments. We will conduct a systematic search of the MEDLINE (OVID), CINAHL (Ebsco), PsycINFO (Ebsco), Embase (OVID), Cochrane Library, Web of Science Core Collection, OpenGrey, GoogleScholar, arXiv.org, and medRxiv.org databases. In addition, targeted searches will be performed to identify grey literature. The review will report information, including but not limited to: demographic characteristics of study and participants, catalogue of digital tools to triage musculoskeletal pain, context of triage tool, and if reported, the development and performance (i.e., accuracy) of the tools.
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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 | 0.016 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.025 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".