Normative values of the Western Ontario Rotator Cuff (WORC) Index for the general population in the USA
Bibliographic record
Abstract
Objectives There are limited data on Western Ontario Rotator Cuff (WORC) Index scores in a population without shoulder pathology. The primary purpose was to provide normative reference values for the WORC in an adult population with healthy shoulders. The secondary purpose was to identify demographic factors and comorbidities that may impact WORC scores. Methods The WORC survey was completed by patients and family members at a suburban outpatient orthopaedic centre. Patients with current shoulder injury or former upper extremity surgery were excluded. Percentage scores were calculated for total and subscale measures. Demographic factors and comorbidities were evaluated to identify possible risk factors for an abnormal baseline score (less than 100). Results There were 988 participants (434 men, 554 women). Overall WORC median (IQR) was 98.64 (94.3 to 100). Each subscale was greater than 96 for all sex-based and age-based subgroups. Median WORC scores were greater than those reported in previous literature following surgery for shoulder pathology. Demographic risk factors for poor WORC scores included body mass index (BMI) greater than 30 (p<0.05), tobacco use (p<0.05), a history of shoulder problems (p<0.001) and a history of shoulder injections (p<0.001). Conclusions This study provides baseline values for the WORC survey in a US suburban population across a range of age groups, demonstrating that surgery for shoulder pathology often results in improved WORC scores, but may not return patients to baseline. Demographic risk factors for lower scores include smoking, BMI above 30, history of shoulder problems or history of shoulder injection. Level of evidence Descriptive epidemiology study; Level of evidence: III.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".