Clinical and radiographic evaluation of double pelvic osteotomy to treat canine hip dysplasia
Bibliographic record
Abstract
ABSTRACT: The purpose of this study was to describe our initial experience with double pelvic osteotomy (DPO) in young dogs affected by hip dysplasia (HD) and to report their postoperative outcome. Seven dogs (four females and three males) were included in our study with mean age 8.3 (±1.7) months, and mean body weight 29.5 (±7.4)Kg. Breeds involved were: Rottweiler (n = 1), Labrador Retriever (n = 3), Golden Retriever (n = 1), Great Dane (n=1) and São Miguel Cattle Dog (n = 1). The most common history complaints were: pelvic limb lameness and pain at hip extension and hip abduction. All surgical procedures consisted of osteotomy of the ilium and pubis, acetabular ventroversion and iliac stabilization with a customized bone plate with seven screws, four screws placed at the cranial fragment and the remaining three in the caudal aspect. Average surgical time was 65.8 (±10.4) minutes and median follow-up assessment was 68 (±15) days. Fracture healing was observed within mean period of 26.3 (±8.9) days. Six patients (86%) had satisfactory outcome and one patient didn’t improve after surgery and had to undergo a total hip replacement. Our results showed that DPO is an effective treatment for HD due to the preservation of pelvic geometry and low postoperative morbidity. Since it is a recent technique, further studies are recommended.
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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 | 0.000 |
| 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".