Systematic review of rotator cuff tears in workers' compensation patients
Bibliographic record
Abstract
BACKGROUND: The burden imposed by workplace rotator cuff (RC) injuries has been reasonably defined. However, literature associated with the demographic characteristics and 'best practices' to manage such injuries among workers' compensation (WC) patients is scant. AIMS: To consolidate the existing literature on full-thickness RC tears among WC patients. Subject, shoulder and injury characteristics were examined to determine if and how WC recipients may differ from their non-compensable counterparts. METHODS: A systematic search (databases, clinical practice guideline web resources, conference proceedings and reference lists) revealed 450 abstracts. Two blinded reviewers independently assessed abstracts for inclusion. Sixty abstracts were subsequently included in a blinded full manuscript review. Seventeen of these manuscripts (3.8% of sample; 11 intervention and 6 determinant) were included in the present review. RESULTS: Previous studies demonstrate that operative interventions are appropriate for full-thickness RC tears as substantial gains in range of motion, strength and quality of life were witnessed within the first post-operative year. Non-operative interventions, including workplace-based work hardening, physical therapy and the use of an early referral system, were shown to improve outcomes. Conflicting results exist with respect to determinants such as age and sex. Importantly, WC patients had consistently poorer outcomes than non-WC patients. CONCLUSIONS: Our results show that although WC patients experience substantial benefits from various treatments for full-thickness RC tears, disparities exist between them and their non-WC counterparts. The lack of WC-specific literature limited our results. Larger studies, particularly ones comparing WC patients with their non-compensable counterparts, are crucial to allow for future evidence-based recommendations.
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 | 0.011 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".