The effect of deep shoulder infections on patient outcomes after arthroscopic rotator cuff repair: a retrospective comparative study
Bibliographic record
Abstract
Introduction: The purpose of this study was to evaluate the effects of deep shoulder infections after RCR on patient outcomes.Methods: A retrospective chart review was conducted involving all patients with deep shoulder infections after arthroscopic RCR (study group).Another group of patients who were matched with the study group by age, gender and rotator cuff tear size, and did not develop deep shoulder infections after arthroscopic RCR were randomly identified (control group).The two groups were compared in terms of time to start physiotherapy, shoulder function, and delay in return to work.Results: There were 10 patients in each group.The mean time to start physiotherapy after surgery was 145.3 (SD=158.8)days for the study group and 40.0 (SD=13.7)days for the control group (p=.051).The average forward elevation of the operated shoulder was 133 (SD=33.4)degrees for the study group, and 172 (SD=12.0)degrees for the control group (p=0.003).The average time to return to work at preoperative level was 5.6 months for the study group and 3 months for the control group.Conclusion: Deep shoulder infections after RCR significantly impedes time to start physiotherapy, shoulder function, and patients' ability to return to work.Level of evidence: III b [retrospective comparative (case-control) study].
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".