Retrospective study ofthe resolution of the cranial cruciate ligament rupture by tibial tuberosity advancement
Bibliographic record
Abstract
Objective: Investigate the clinical results, evolution of the patients and possible complications that have \narisen after the tibial tuberosity advancement (TTA). \nStudy design: Retrospective case. \nAnimals: Dogs (n= 29) with cranial cruciate ligament (CrCL) deficiency treated with TTA. \nMethods: Medical records of TTAs performed between 2015 to 2018. One of the technique most \nfrequently used in veterinary to resolve cranial cruciate rupture in dog is tibial transposition advancement. \nThis was introduced in 2002 in veterinary medicine by Montavon, Damur and Tepic[1] \n. In this the \ndiagnosis of a ruptured CrCL was determined on the basis of physical examination findings, including \nhind limb lameness with signs of pain localized to the stifle joint, palpation of medial buttress and signs \nof pain on stifle joint hyperextension, positive cranial drawer or tibial thrust findings, and radiographic \nevidence. The technique was performed according protocol without any modification that affects the \nresults after the surgery and the dogs were not subjected to any type of exclusion. In-hospital re evaluation of limb function and time to radiographic healing were reviewed. Further follow-up was \nobtained by telephone interview of owners. \nResult: The numbers of dogs submitted in this study were a total of 29 (median age, 7 years; median \nbody weight, 32,5 kg). A total of 40 TTA was performed in 4 years and 11 dogs had had TTAs performed \non both stifle joints. The breeds more represent were, mixed (28%), Labrador retrievers (21%) and Dogo \nCanario (14%). \nConclusion: Clinical outcome and owner evaluations in this case series indicate favourable results can be \nexpected when CrCL deficient stifles are treated with TTA.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".