Team-based musculoskeletal assessment and healthcare quality indicators: A systematic review
Bibliographic record
Abstract
The primary objective of this review was to describe health quality indicator (HQI) outcomes of team-based musculoskeletal (MSK) assessments aimed at directing patient care. Secondary objectives included determining the most commonly assessed HQIs, extent of team collaboration, and the healthcare practitioners that most commonly comprise MSK-assessment teams. This review was registered in the PROSPERO database and conducted according to PRISMA guidelines. Five databases were systematically searched to August 2017. Studies selected met a priori inclusion criteria and investigated an HQI outcome of a primary or intermediate care MSK team-based assessment aimed at directing treatment. Two independent raters assessed study quality [Downs and Black (DB) criteria] and level of evidence (Oxford Centre of Evidence-Based Medicine model). Ten studies were included. The majority were low-quality [median DB score 14/32 (range 6-18)] pre-experimental studies (level 4 evidence). Heterogeneity in methodology and HQIs precluded meta-analyses. Hospital length-of-stay (LOS; 3/10 studies) and pain level (3/10) were the most common HQIs investigated. Teams (9/10) were most commonly comprised of a physiotherapist and another healthcare practitioner. Most teams (8/10) demonstrated low-levels of collaboration. There is limited low-level evidence to suggest that team-based MSK assessments are associated with improved clinical outcomes (i.e., pain, quality-of-life) and shorter LOS.
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 | 0.021 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".