Effect of doxycycline on contra lateral canine cranial cruciate ligament rupture
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether doxycycline administered to dogs with unilateral cranial cruciate ligament rupture (Uni-CCLR) would decrease the risk of contralateral-CCLR (Co-CCLR). To evaluate predictors for Co-CCLR survival. To evaluate if a predisposition of Labrador Retrievers to Co-CCLR exists when compared to other breeds. METHODS: In this prospective randomized controlled clinical trial, 69 client-owned dogs with Uni-CCLR were randomly assigned to a doxycycline (group-D: 7.5 mg/kg PO BID x 6 weeks) or non-doxycycline (group-ND: negative control). Medical and imaging data, time from Uni- to Co-CCLR and to follow-up were recorded. Statistics included chi-squared test, logistic regression, Kaplan-Meier survival analysis, log rank test, survival curves, and frailty model (p <0.05). RESULTS: This study included 32 dogs in group-D, and 37 dogs in group-ND. Median follow-up was 54.5 and 61 months, respectively. Contralateral CCLR occurred in 53.1% and 48.6% at medians of 20 and 11 months, respectively. Doxycycline did not significantly decrease the risk of Co-CCLR (p = 0.83). This risk was decreased by 14.2% with each year of age but increased with each increasing kilogram of body weight and each increasing degree of tibial plateau angle by 5.4% and 9.7%, respectively. Labrador Retrievers were not significantly predisposed (p = 0.37). CLINICAL SIGNIFICANCE: At the dose regimen investigated doxycycline does not decrease the risk for Co-CCLR.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".