Even Experts Can Be Fooled: Reliability of Clinical Examination for Diagnosing Hip Dislocations in Newborns
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to assess the accuracy of clinical screening examination in newborns with dislocated hips compared with ultrasound scan (USS). METHODS: Newborns, up to 3 months of age, with confirmed hip dislocations on USS were prospectively enrolled in a multinational observational study. Data from 2010 to 2016 were reviewed to determine pretreatment clinical examination findings of the treating orthopaedic surgeon as well as baseline ultrasound indices of developmental dysplasia of the hip (DDH). All infants had been referred to specialist centres with expertise in DDH, due to abnormal birth examination or risk factor. RESULTS: The median age of the study population was 2.3 weeks and 84% of patients were female. Of the total 515 USS-confirmed dislocated hips included in the study, 71 (13.8%) were incorrectly felt to be reduced on clinical examination by the treating orthopaedist (P<0.001). Full hip abduction was documented in 106 hips. Of the hips correctly identified as dislocated, 322 hips were further analyzed based on clinical reducibility. Thirty-three of 322 (10.2%) were incorrectly thought to be reducible when in fact they were irreducible or vice versa. CONCLUSIONS: Expert examiners missed a significant number of frankly dislocated hips on clinical examination and their ability to classify hips based on clinical reducibility was only moderately accurate. This study provides evidence that, even in experienced hands, physical examination findings in DDH are often too subtle to elicit clinically in the first few months of life. This may explain the persistent and measurable rate of late presenting dislocations in countries with screening programmes reliant on clinical examination. LEVEL OF EVIDENCE: Level 1-testing of previously developed diagnostic criteria in series of consecutive patients (with universally applied reference "gold" standard).
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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 | 0.008 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".