Radiographic Evaluation of the Hip has Limited Reliability
Bibliographic record
Abstract
Radiographic evaluation provides essential information regarding the diagnosis and treatment of musculoskeletal disorders. We evaluated the ability of hip specialists to reliably identify important radiographic features and to make a diagnosis based on plain radiographs alone. Five hip specialists and one fellow performed a blinded radiographic review of 25 control hips, 25 hips with developmental dysplasia (DDH), and 27 with femoroacetabular impingement (FAI). On two separate occasions, readers assessed acetabular version, inclination and depth, position of the femoral head center, head sphericity, head-neck offset, Tönnis grade, and joint congruency. Observers made a diagnosis categorizing each hip as normal, dysplastic, FAI, or combined DDH and FAI (features of both). Reliability was determined using Cohen's kappa coefficient. Intraobserver values were highest for acetabular inclination (kappa = 0.72) and determination of femoral head center position (kappa = 0.77). Interobserver reliability values were highest for acetabular inclination (kappa = 0.61) and Tönnis osteoarthritis grade (kappa = 0.59). All other measurements, including diagnosis, had kappa values less than 0.55. We concluded many of the standard radiographic parameters used to diagnose DDH and/or FAI are not reproducible. Accordingly, a more clear set of definitions and measurements must be developed to allow for more reliable diagnosis of early hip disease.
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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 | 0.098 | 0.246 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".