Do Medical Comorbidities Affect Outcomes in Patients With Rotator Cuff Tears?
Bibliographic record
Abstract
Background: The effects of medical comorbidities on clinical outcomes in patients with rotator cuff tears (RCTs) have not been fully elucidated. This study investigates the association between medical comorbidities, as measured by the Functional Comorbidity Index (FCI), and clinical outcomes in patients treated surgically or nonsurgically for symptomatic, full-thickness RCTs. Hypothesis: Patients with RCTs who have more comorbidities will have worse outcome scores. Study Design: Cohort study; Level of evidence, 3. Methods: We collected the following outcome measures at baseline and at regular intervals up to 64 weeks in all patients: FCI, the Western Ontario Rotator Cuff Index (WORC), and the American Shoulder and Elbow Surgeons (ASES) score. Changes in outcomes were compared separately for surgical and nonsurgical patients using paired t tests. The relationship of the FCI and all outcomes of interest at baseline, at 64-week follow-up, and for changes from baseline was explored using linear regression modeling. Results: Of the 222 study patients (133 males; mean age, 60.0 ± 9.6 years), 140 completed the 64-week WORC and 120 completed the 64-week ASES. Overall, 128 patients underwent RCT repair, and 94 patients were treated nonsurgically. Both treatment groups improved compared with baseline at 64 weeks on the ASES score and WORC. At 64 weeks, patients with higher baseline FCI scores had worse WORC score (by 74.5 points; P = .025) and ASES score (by 3.8 points; P < .01). A higher FCI score showed a trend toward predicting changes in the WORC and ASES scores at 64 weeks compared with baseline, but this did not reach statistical significance (WORC change, P = .15; ASES change, P = .07). Conclusion: Patients with higher FCI scores at baseline reported worse baseline functional scores and demonstrated less improvement with time. The magnitude of this change may not be clinically significant for single comorbidities.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".