Reliability of the sourcil method of acetabular index measurement in developmental dysplasia of the hip
Bibliographic record
Abstract
PURPOSE: The ability to monitor and study developmental dysplasia of the hip (DDH) requires validated radiographic outcome measures. The sourcil method of acetabular index measurement (AI-S) has not yet been shown to be a reliable measure of acetabular dysplasia in a DDH population, despite its widespread use. The aims of this study were to test the reliability of the AI-S method in a DDH population, and to compare the reliability of the AI-S method with that of the classic lateral edge method (AI-L). METHODS: From an institutional database, standardized anteroposterior hip radiographs were obtained from a cohort of 35 female patients (70 hips) at two and five years of age who had been treated nonoperatively for DDH. Three observers independently measured the acetabular index using the AI-L and AI-S methods on all 70 hips at two time points, four weeks apart. RESULTS: The inter-rater reliability intraclass correlation coefficient (ICC) for the AI-L and AI-S methods was between good and excellent at 0.94 (confidence interval (CI) 0.89 to 0.96) and 0.91 (CI 0.87 to 0.94), respectively. The ICCs for intra-rater reliability for the AI-L method were excellent at 0.93 (CI 0.90 to 0.95), 0.95 (CI 0.93 to 0.97) and 0.95 (CI 0.94 to 0.97) for raters 1, 2 and 3, respectively. The ICCs for intra-rater reliability for the AI-S method were between good and excellent at 0.91 (CI 0.87 to 0.93), 0.93 (CI 0.90 to 0.95) and 0.90 (CI 0.86 to 0.93) for raters 1, 2 and 3 respectively. CONCLUSION: Both AI-S and AI-L methods are equally reliable radiographic measures of DDH. LEVEL OF EVIDENCE: Level III (diagnostic).
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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 | 0.015 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".