Validity and Reliability of a Novel 3D Measurement Approach of the Acetabulum
Bibliographic record
Abstract
The hip joint is a frequent site of osteoarthritis. Advances in diagnosis and treatment are limited due to the lack of reliable measures for quantifying hip morphology. Current diagnostic measures of the hip are performed with predetermined measures in 2D planes, but these do not fully account for morphological variation nor do they utilize 3D capabilities of CT and MR. A valid and reliable measurement modality describing the entire geometry of the hip is necessary for early diagnosis and treatment of hip disease. A 3D measurement approach was developed and proved to be valid and reliable for the proximal femur. The purpose here was to assess the validity and reliability of the same 3D approach on acetabular morphometry. The technique was applied to 45 cadaveric acetabuli (23 Right; 9 Female) and their digitally reconstructed CT models. Preliminary results indicate this measurement approach is not valid with significant differences detected between the cadaveric and digital measurements, p < 0.05. However, the digital measurements had excellent intraobserver reliability (ICC = 0.99) and interobserver reliability (ICC = 0.94). Although this approach was valid and reliable for proximal femora it appears reliable, but not valid for measuring the acetabuli. This result is due to the presence of calcified labra being visualized as bone, and being included in the bone measurements, when measuring the digital models. Grant Funding Source : Departmental Funding
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".