Impact of smoking on patient outcomes after arthroscopic rotator cuff repair
Bibliographic record
Abstract
BACKGROUND: Cigarette smoking may adversely affect rotator cuff tear pathogenesis and healing. However, the impact of cigarette smoking on outcomes after arthroscopic rotator cuff repair is relatively unknown. PATIENTS AND METHODS: A cohort of 126 patients who underwent arthroscopic rotator cuff repair with minimum 2 years follow-up were retrospectively identified from our institutional database. Patient demographics, comorbidities, and cuff tear index were collected at initial presentation. Outcome measures including American Shoulder and Elbow Surgeons (ASES) score, Western Ontario Rotator Cuff (WORC) score and Visual Analogue Scale (VAS) for pain were collected at each clinical follow-up. Mixed model regression analysis was used to determine the impact of smoking on outcomes, while controlling for tear size and demographics. RESULTS: In our cohort, 14% were active or recent smokers. At baseline, smokers presented with higher pain, greater comorbidities and worse ASES scores than non-smokers. Smokers also had a non-significant trend towards presenting for surgical repair at a younger age and with larger tear sizes. Both smokers and non-smokers had statistical improvements in outcomes at 2 years following repair. Regression analysis revealed that smokers had a worse improvement in ASES but not WORC or VAS pain scores after surgery. CONCLUSION: The minimal clinically important difference was achieved for ASES, WORC and VAS pain in both smokers and non-smokers, suggesting both groups substantially benefit from arthroscopic rotator cuff repair. Smokers tend to present with larger tears and worse initial outcome scores, and they have a lower functional improvement in response to 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".