Excessive Apoptosis in Patellar Tendinopathy in Athletes
Bibliographic record
Abstract
BACKGROUND: The pathogenesis of tendon overuse injuries is poorly understood. The histopathology underlying tendinopathy at various anatomical locations is similar and may reflect a common pathologic process. HYPOTHESIS: Apoptosis contributes to the pathophysiology in patellar tendinopathy. STUDY DESIGN: Case control study; Level of evidence, 3. METHODS: We compared biopsy specimens from the patellar tendon in patients with patellar tendinopathy diagnosed clinically and with typical magnetic resonance image findings with biopsy specimens from a control group without any previous or current knee complaints to suggest patellar tendinopathy. The presence of apoptosis was examined with immunohistochemical methods using a polyclonal antibody recognizing active caspase-3, confirmed by labeling DNA strand breaks (F7-26 antibody) and nuclear morphology (fragmentation and condensation). RESULTS: The number of apoptotic cells per unit area (4.5 mm(2)) was 0.91 +/- 0.81 (SD) in tendinopathic samples and 0.21 +/- 0.21 in controls (P = .026). Although the tendinopathic samples displayed increased cellularity (average 162.5 nuclei/mm(2) vs 98.9 nuclei/mm(2)), the apoptotic index was higher (0.42% vs 0.17%, P = .014). CONCLUSION: Increased apoptotic cell death is a feature of patellar tendinosis. The role of apoptosis within the broader framework and time course of tendon overuse injury remains to be established.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".