Increased Diagnostic Information and Understanding Disease: Uncertainty in the Diagnosis of Developmental Hip Dysplasia
Bibliographic record
Abstract
Great advances have been made in developing strategies to improve the quality of medical care in the past decade; these advances include better diagnostic technologies, such as ultrasonography (US), computed tomography, and magnetic resonance imaging. Although these tests provide new information on many conditions, such as developmental dysplasia of the hip (DDH), the differentiation of what is normal, what is abnormal, and what is disease is no longer intuitive. Historically, the diagnosis of DDH was straightforward. The diagnosis was based primarily on clinical findings, which were often confirmed with radiography. Abnormal hips were either subluxated or dislocated and, if left untreated, adverse consequences were certain in either situation. Since the introduction of hip US, however, increased diagnostic sophistication has led to uncertainty as to how to interpret the continuous spectrum of acetabular morphology. There is no consensus on the degree of acetabular dysplasia that does or does not require treatment. Because not every abnormal finding may require treatment, the terms abnormality and disease are not synonymous.
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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 | 0.102 | 0.272 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.010 | 0.003 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.010 | 0.023 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".