A three‐dimensional measurement approach for the morphology of the femoral head
Bibliographic record
Abstract
The hip joint is one of the most frequent sites of osteoarthritis. Advances in diagnosis and clinical treatment have progressed dramatically in the last few decades; however, there are limitations associated with the lack of reliable measures for quantifying hip joint morphology. Current diagnostic measures of the hip are performed with pre-determined measures, typically lengths and angles, on 2D radiographic planes. The current measurement techniques do not utilize the inherent 3D nature of CT and MR imaging and do not necessarily quantify the relevant clinical pathologies. A valid and reliable measurement modality that measures the surface geometry of the femoral head is necessary for early diagnosis and treatment of hip disease. The purpose of this study was to establish a method to quantify femoral head morphology using a three-dimensional model. A novel measurement approach was applied to 45 cadaveric femurs (23 right; 22 left; nine female, 17 male) and their digitally reconstructed 3D CT models. The mean difference between the cadaveric and digital measures was -2.04 mm with 95% confidence limits (CI) of 13.67 mm and -17.75 mm, respectively. The digital measurement approach was found to have excellent intraobserver reliability (ICC = 0.99, CI 0.98-0.99) and interobserver reliability (ICC = 0.98, CI 0.93-0.99). This valid and reliable novel digital measurement approach enables quantification of the 3D surface geometry of the femoral head and is able to measure individual variations and potentially detect abnormalities. This method may be used to assist future studies to establish valid diagnostic measurements for femoral head and head-neck junction pathologies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 0.001 |
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".