Inter‐ and intra‐observer agreement of femoroacetabular impingement (FAI) parameters comparing plain radiographs and advanced, 3D computed tomographic (CT)‐generated hip models in a surgical patient cohort
Bibliographic record
Abstract
PURPOSE: The purpose of our study was to investigate whether advanced, 3D computed tomographic (CT)-generated hip models improves inter-and intra-observer agreement when compared to plain radiographs in identifying femoroacetabular impingement (FAI) morphology. METHODS: Eight consecutive patients who underwent surgery for FAI pathology were selected for this study. Preoperative CT scan image data were used to create high resolution, 3D hip reconstruction models. Four observers (two attending hip surgeons and radiologists) performed a blinded review of preselected radiographs and 3D CT hip models. Alpha and lateral center-edge angle measurements, location of cam lesion and the presence of a "crossover sign" were assessed. Inter- and intra-observer agreement was determined by calculating the intra-class correlation coefficients (ICC) or kappa coefficients to evaluate agreement for categorical variables. RESULTS: The parameter that demonstrated the highest and poorest inter-observer agreement was the presence of a "crossover sign" using 3D CT-generated high resolution hip models (ICC = 0.76, p = 0.00) and anteroposterior pelvis radiography, respectively (ICC = 0.20, p = 0.02). Alpha angle values were significantly higher using plain radiographs when compared to 3D hip reconstruction models (61.1° ± 10.4° versus 55.4° ± 14.4°, p = 0.003). Furthermore, when compared to radiographs, 3D hip reconstruction models demonstrated significantly higher intra-observer agreement (ICC = 0.856 versus 0.405, p = 0.005) when determining the presence of a "crossover sign". CONCLUSIONS: Our findings were suggestive that for most commonly used FAI morphology parameters, CT-generated hip models demonstrated little benefit over plain radiographs in improving inter-observer agreement among providers. LEVEL OF EVIDENCE: III.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".