Quantifying Dynamic Assessment of Developmental Dysplasia of the Hip
Bibliographic record
Abstract
Although physical and ultrasound (US)-based screening for congenital deformities of the hip (developmental dysplasia of the hip, or DDH) is routinely performed in most countries, one of the most commonly performed maneuvers done under ultrasound observation - dynamic assessment - has been shown to be relatively unreliable and is associated with significant misdiagnosis rates, on the order of 29%. Our overall research objective is to develop a quantitative method of assessing hip instability, which we hope will standardize diagnosis across different raters and health-centers, and may perhaps improve reliability of diagnosis. To quantify dynamic assessment, we propose to use the variability in femoral head coverage (FHC) measurements within multiple US scans collected during a dynamic assessment. In every US scan, we use our recently-developed automatic FHC measuring tool which leverages phase symmetry features to approximate vertical cortex of ilium and a random forest classifier to identify approximate location of the femoral head. Having estimated FHC in each scan, we estimate the change in FHC across all the US scans during a dynamic assessment and compare this change with variability of FHC found in previous studies. Our findings - in a dynamic assessment on an infant done by an orthopaedic surgeon, the femoral centre moved by up to 19% of its diameter during distraction, from 55% FHC to 74% FHC. This change in FHC is slightly greater than its variability in static US scans reported in previous studies, suggesting that the distraction force likely produced a real lateral displacement. Our clinician’s qualitative assessment concluded the hip to be normal as this degree of distraction was not indicative of instability. This suggests that our technique likely has sufficient resolution and repeatability to quantify differences in laxity between stable and unstable hips, although this presumption will have to be confirmed in a subsequent study with additional subjects. The long-term significance of this approach to evaluating dynamic assessments may lie in increasing early diagnostic sensitivity in order to prevent dysplasia remaining undetected prior to manifesting itself in early adulthood joint 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 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".