EFFECTS OF PELVIC AND FEMORAL POSITIONING ON CANINE NORBERG ANGLE MEASUREMENTS AND TEST–RETEST RELIABILITY: A COMPUTED TOMOGRAPHY-BASED SIMULATION STUDY
Bibliographic record
Abstract
Canine hip dysplasia is a common disease in dogs, often diagnosed by using the Norberg angle (NA), an index for the laxity of the hip joint. Measurement of the NA can be affected by the pelvic and femoral positioning during imaging, the effects and test–retest reliability of which have not been documented. To bridge the gap in knowledge, computed tomography data from 11 Labrador Retriever dogs were obtained and used to generate synthetic ventrodorsal radiographs of the hip for NA measurements via a perspective projection model. Twenty-five synthetic radiographs of the hips were generated at positions defined by combinations of five pelvic tilt angles (-20° to 20° at 10° intervals) and five femoral elevation angles (from full extension to 40° at 10° intervals). For each radiograph, the NA was measured three times by each of the two experienced veterinarian examiners. It was found that both the increase in caudal pelvic tilt and femoral elevation increased the measured NA, although the intra- and inter-examiner reliability was very good for a given hip position. The current results suggest that careful positioning of the pelvis and femur during radiographic imaging is critical for accurately measuring the NA, and thus the laxity of the hip, for the clinical diagnosis of hip dysplasia.
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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 | 0.017 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".