Sex and gender disparity in pathology, disability, referral pattern, and wait time for surgery in workers with shoulder injury
Bibliographic record
Abstract
BACKGROUND: The role of sex as an important biological determinant of vulnerability to sustaining injury and gender as a social determinate of access to resources, referral for medical care and perceived disability remains conflicted in injured workers. The purpose of this study was to examine sex and gender disparity following a compensable work-related shoulder injury. METHODS: This study involved cross-sectional analyses of data of two independent samples of workers with shoulder injury. Measures of disability and pain were the Quick Disabilities of the Arm, Shoulder and Hand (QuickDASH) and Numerical Pain Rating Scale (NPRS) for patients seen at an Early Shoulder Physician Assessment (ESPA) program and the American Shoulder and Elbow Surgeons (ASES) assessment form and Visual Analogue Scale (VAS) for the sample who underwent surgery. RESULTS: The files of 1000 (443 females, 557 men) consecutive patients seen at an ESPA program and 150 (44 females, and 106 men) consecutive patients who underwent rotator cuff surgery (repair or decompression) were reviewed. Significant gender disparity was observed in the referral pattern of injured workers seen at the ESPA program who were referred for surgical consultation (22 vs. 78 % for females and males respectively, p < 0.0001). The independent rotator cuff surgical group had a similar gender discrepancy (29 % vs. 71 %, p < 0.0001). The timeframe from injury to surgery was longer in women in the surgical group (p = 0.01). As well, women waited longer from the date of consent to date of surgery (p = 0.04). Women had higher incidence of repetitive injuries (p = 0.01) with men reporting higher incidence of falls (p = 0.01). Women seen at the ESPA program were more disabled than men (p = 0.02). Women in both samples had a higher rate of medication consumption than men (p = 0.01 to <0.0001). Men seen at the ESPA program had a higher prevalence of full thickness rotator cuff tears (p < 0.0001) and labral pathology (p = 0.01). However, these pathologies did not explain gender disparity in the subsample of ESPA who were referred for surgical consultation or those who had surgery. CONCLUSIONS: Sex and gender disparity exists in workers with shoulder injuries and is evident in the mechanism of injury, perceived disability, medication consumption, referral pattern, and wait time for surgery.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".