The Familial Predisposition toward Tearing the Anterior Cruciate Ligament
Bibliographic record
Abstract
PURPOSE: A study of 171 surgical cases and 171 matched controls was conducted to investigate whether a familial predisposition toward tearing the anterior cruciate ligament of the knee exists. STUDY DESIGN: Case control study; Level of evidence, 3. METHODS: Patients who were diagnosed with an anterior cruciate ligament tear were matched by age (within 5 years), gender, and primary sport to subjects without an anterior cruciate ligament tear. All 342 subjects completed a questionnaire detailing their family history of anterior cruciate ligament tears. RESULTS: When controlling for subject age and number of relatives, participants with an anterior cruciate ligament tear were twice as likely to have a relative (first, second, or third degree) with an anterior cruciate ligament tear compared to participants without an anterior cruciate ligament tear (adjusted odds ratio = 2.00; 95% confidence interval, 1.19-3.33). When the analysis was limited to include only first-degree relatives, participants with an anterior cruciate ligament tear were slightly greater than twice as likely to have a first-degree relative with an anterior cruciate ligament tear compared to participants without an anterior cruciate ligament tear (adjusted odds ratio = 2.24; 95% confidence interval, 1.24-4.00). CONCLUSIONS: Findings are consistent with a familial predisposition toward tearing the anterior cruciate ligament. CLINICAL RELEVANCE: Future research should concentrate on identifying the potentially modifiable risk factors that may be passed through families and developing strategies for the prevention of anterior cruciate ligament injuries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".