Adding false-profile radiographs improves detection of developmental dysplasia of the hip, data from the CHECK cohort
Bibliographic record
Abstract
The aim of this study was to determine the additional value of the false-profile (FP) view radiograph in the diagnosis of developmental dysplasia of the hip (DDH), as compared with an anteroposterior (AP) pelvic radiograph only, and evaluate the correlation between the Wiberg-lateral center edge angle (W-LCEA) and Wiberg-anterior center edge angle (W-ACEA). We used baseline data from a nationwide prospective cohort study (Cohort Hip and Cohort Knee). DDH was quantified on AP pelvic and FP hip radiographs using semi-automatic measurements of the W-LCEA and W-ACEA. A threshold of <20° was used to determine DDH for both the W-LCEA and the W-ACEA. The proportion of DDH only present on the FP view determined the FP view additional value. The correlation between the W-LCEA and W-ACEA was determined. In total 720 participants (1391 hips) were included. DDH was present in 74 hips (5.3%), of which 32 were only present on the FP view radiograph (43.2%). The Pearson correlation coefficient between W-LCEA and W-ACEA of all included hips was 0.547 (95% confidence interval: 0.503-0.591) and 0.441 (95% confidence interval: 0.231-0.652) in hips with DDH. A mean difference of 9.4° (SD 8.09) was present between the W-LCEA and the W-ACEA in the hips with DDH. There is a strong additional value of the FP radiograph in the diagnosis of DDH. Over 4 out of 10 (43.2%) individuals' DDH will be missed when only using the AP radiograph. In hips with DDH a moderate correlation between W-LCEA and W-ACEA was calculated indicating that joints with normal acetabular coverage on the AP view can still be undercovered on the FP view.
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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 | 0.003 | 0.014 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".