Management of Acute Work-Related Shoulder Injuries by an Early Shoulder Assessment Program: Efficiency of Imaging Investigations
Bibliographic record
Abstract
Purpose: There has been a significant increase in the number of costly investigations of the shoulder joint over the past decade. The purposes of this study were to (1) describe the diagnostic imaging investigations ordered for injured workers seen at an Early Shoulder Physician Assessment (ESPA) program, (2) evaluate the impact of these investigations on final diagnosis and management, and (3) examine how efficient the program was by determining the appropriateness of referrals and whether costly imaging was justified. Methods: This was a retrospective review of the electronic files of injured workers who had been referred to an early assessment program because they had not progressed in their recovery or return-to-work plan within 16 weeks of the injury or reoccurrence. Results: The data of 750 consecutive patients—337 women (45%) and 413 men (55%), mean age 49 (SD 11) years—were reviewed. A total of 183 patients (24%) had been referred for further investigation. Of these, 90 (49%) were considered candidates for surgery (group 1), 58 (32%) had a change in diagnosis or management (group 2), and 17 (9%) had no change in diagnosis or management (group 3); 18 (10%) patients were lost to follow-up. We noticed a pattern in the type of diagnosis and the groups: full-thickness rotator cuff (RC) tear was the predominant diagnosis (Fisher's exact test [FET]=0.001, p<0.0001) for group 1. No statistically significant differences were found among the groups in the prevalence of labral pathology (FET=0.010, p=0.078), impingement syndrome (FET=0.012, p=0.570), partial-thickness RC tear (FET=0.004, p=0.089), or biceps pathology (FET=0.070, p=0.149). Ultrasound investigations were more prevalent in group 2 (FET=0.004, p=0.047). No pattern was found for use of magnetic resonance imaging and group allocation. However, all magnetic resonance arthrogram investigations (FET=0.007, p=0.027) had been ordered for patients who required labral or instability-related surgery. Conclusions: Of the injured workers we studied, 24% had further investigation, and the type and severity of pathology had affected the type of investigation. For the 165 patients who were included in groups 1–3, the ESPA was 90% efficient, with only 10% of patients not having had a change in diagnosis or management.
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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 | 0.004 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".