Minimal clinical data sets for spine-related musculoskeletal disorders in primary care and outpatient settings: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Lack of standardised clinical data collection may lead to reduced quality in musculoskeletal (MSK)-related clinical care and research. Little is known about the availability and characteristics of minimal clinical data sets for spine-related MSK disorders in primary care and outpatient settings and their utility for improving healthcare quality. Our objective is to undertake a scoping review aiming to identify and map current literature on minimal clinical data sets for measuring and monitoring health status in patients with spine-related MSK disorders in primary and outpatient healthcare settings. METHODS AND ANALYSIS: The 2020 Joanna Briggs Institute methodology for scoping reviews will guide review conduct. The review will consider studies that describe and report on minimal clinical data sets for spine-related MSK disorders designed for primary care and outpatient clinical practice settings. Quantitative and qualitative study designs will be eligible, including consensus-based studies, interventional, observational, feasibility and linguistic validation studies. Studies published in English, German, French, Italian and Spanish will be included, with no limit on date of publication. MEDLINE, CINAHL, Cochrane Library, Index to Chiropractic Literature, MANTIS, ProQuest Dissertations & Theses Global and medRxiv preprint repository will be searched from database inception to 25 July 2021. Two reviewers will independently screen identified titles, abstracts and relevant full-text records, and then extract data using review-specific data extraction forms. Findings will be synthesised and presented as a descriptive summary using PRISMA ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). ETHICS AND DISSEMINATION: Ethics review and approval is not required for this scoping review. Our target audience for this review will be clinicians, researchers, patients and other relevant stakeholders involved in the measurement and health status monitoring of patients with spine-related MSK disorders. Results will be shared through peer-reviewed publication and presentations at relevant conferences. PROTOCOL REGISTRATION NUMBER: https://osf.io/fkw5b.
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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 | 0.185 | 0.157 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.022 | 0.019 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.057 | 0.017 |
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".