The lateral edge and sourcil acetabular indices for surgical decision-making in developmental dysplasia of the hip
Bibliographic record
Abstract
PURPOSE: The acetabular index (AI) is a radiographic measure that guides surgical decision-making in developmental dysplasia of the hip (DDH). Two AI measurement methods are described; to the lateral edge of the acetabulum (AI-L) and to the lateral edge of the sourcil (AI-S). The purpose of this study was to determine the level of agreement between AI-L and AI-S on the diagnosis and degree of acetabular dysplasia in DDH. METHODS: A total of 35 patients treated for DDH with Pavlik harness were identified. The AI-L and AI-S were measured on radiographs (70 hips) at two and five years of age. AI-L and AI-S were then transformed relative to published normative data (tAI-L and tAI-S). Bland-Altman plots, linear regression and heat mapping were used to evaluate the agreement between tAI-L and tAI-S. RESULTS: There was poor agreement between tAI-S and tAI-L on the Bland-Altman plots with wide limits of agreement and no proportional bias. The two AI measurements were in agreement as to the presence and severity of dysplasia in only 63% of hips at two years of age and 81% at five years of age, leaving the remaining hips classified as various combinations of normal, mildly and severely dysplastic. CONCLUSION: AI-L and AI-S have poor agreement on the presence or degree of acetabular dysplasia in DDH and cannot be used interchangeably. Clinicians are cautioned to prudently evaluate both measures of AI in surgical decision-making. LEVEL OF EVIDENCE: I.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".