Distinguishing transient osteoporosis of the hip from avascular necrosis.
Bibliographic record
Abstract
INTRODUCTION: To review the circumstances surrounding the misdiagnosis of transient osteoporosis of the hip (TOH) as avascular necrosis (AVN) and to increase physician awareness of the prevalence and diagnosis of this condition in young men, we reviewed a series of cases seen in the orthopedic unit at St. Michael's Hospital, University of Toronto. METHODS: We studied the charts of patients with TOH referred between 1998 and 2001 with a diagnosis of AVN for demographic data, risk factors, imaging results and outcomes. RESULTS: Twelve hips in 10 young men (mean age 41 yr, range from 32-55 yr) were identified. Nine men underwent magnetic resonance imaging (MRI) before referral, which showed characteristic changes of TOH. All 10 patients were referred for surgical intervention for a diagnosis of AVN. The correct diagnosis was made after reviewing patients' charts and the scans and was confirmed by spontaneous resolution of both symptoms and MRI findings an average of 5.5 months and 7.5 months, respectively, after consultation. CONCLUSIONS: Despite recent publications, the prevalence of TOH among young men is still overlooked and the distinctive MRI appearance still misinterpreted. Symptoms may be severe but resolve over time with reduced weight bearing. The absence of focal changes on MRI is highly suggestive of a transient lesion. A greater level of awareness of this condition is needed to differentiate TOH from AVN, avoiding unnecessary surgery and ensuring appropriate treatment.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".