Influence of radiographic techniques on the measurement of femoral anteversion angles and a conformation score of pelvic limbs in<scp>L</scp>abrador retrievers
Bibliographic record
Abstract
OBJECTIVE: To determine repeatability of and correlation between 2 radiographic measurements of femoral anteversion angles (FAA) and to determine their influence on a score derived from tibial plateau angle (TPA) and FAA to predict the risk of cranial cruciate ligament disease (CCLD). STUDY DESIGN: Prospective clinical study. ANIMALS: Forty-eight Labrador retrievers with or without CCLD. METHODS: FAA and CCLD scores were calculated for each limb from extended pelvic radiographs (t-FAA) or angled (a-FAA) projections of the femur by 3 investigators. One investigator repeated measurements twice. Data were analyzed for repeatability, correlation between t-FAA and a-FAA, and their influence on CCLD scores. RESULTS: FAA correlated most strongly with the distance between the femoral head and the femoral axis on mediolateral radiographs, a measurement with excellent repeatability. t-FAA and a-FAA correlated with each other (r > 0.79, P < .0001), although t-FAA were about 1° greater than a-FAA (P = .01). Intrainvestigator and interinvestigator repeatability of the CCLD score was fair when derived from t-FAA and good to excellent when derived from a-FAA. CCLD scores differed between radiographic techniques but led to different predictions in only 9 (10%) limbs, all with lower TPA and CCLD scores than the rest of the population. CONCLUSION: a-FAA correlated strongly with t-FAA and improved the repeatability of CCLD scores within and between investigators. CLINICAL SIGNIFICANCE: A craniocaudal angled beam projection of the femur is a suitable alternative to a ventrodorsal pelvic radiograph when measuring FAA and may improve the repeatability and positive predictive value of CCLD scores.
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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 | 0.004 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".