Rotator Cuff Tear Degeneration and Cell Apoptosis in Smokers Versus Nonsmokers
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to assess the effect of smoking on supraspinatus tendon degeneration, including cellular alterations, proliferation, and apoptosis of tendon cells. METHODS: Supraspinatus tendon samples of 10 smokers and 15 nonsmokers with full-thickness tears were compared, focusing on the severity of tendon histopathology including apoptosis (programmed cell death), cellularity, and proliferation. Immunohistochemistry was used to assess the density of apoptotic cells and proliferation. The extent of tendon degeneration was classified according to a revised version of the Bonar tendon histopathology score. RESULTS: The smokers were younger (P = .01). The symptom duration among smokers was longer (P < .05). The supraspinatus tendons from the smokers presented significantly more advanced degenerative changes (Bonar score, 13.5 [interquartile range, 1.4] v 9 [interquartile range, 3]; P < .001). The smokers' tendons showed increased density of apoptotic cells (0.108 [SE, 0.038] v 0.0107 [SE, 0.007]; P = .024) accompanied by reduced tenocyte density (P = .019) and upregulation of proliferative activity (P < .0001). CONCLUSIONS: Smoking is associated with worsened supraspinatus tendon histopathology and increased apoptosis. CLINICAL RELEVANCE: Pronounced degenerative changes, reduced tendon cellularity, and increased apoptosis may indicate reduced tendon healing capacity in smokers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".