The heterogeneous management of pediatric ankle traumas
Bibliographic record
Abstract
Frequent misdiagnosis of pediatric ankle traumas leads to inappropriate management, which may result in residual pain, instability, slower return to physical activity, and long-term degenerative changes. The purpose of this study was to evaluate the consistency of diagnosis, management, and the treatment of acute lateral pediatric ankle trauma in a tertiary care pediatric hospital. The hypothesis was that the initial diagnosis is often incorrect, and the treatment varies considerably amongst orthopedic surgeons.We conducted a retrospective study of all cases of ankle sprains and Salter-Harris one (SH1) fractures referred to our orthopedic surgery service between May and August 2014. Exclusion criteria included ankle fractures other than SH1 types, and cases where treatment was initially undertaken elsewhere before referral to our service. Primary outcome was the difference between initial and final diagnosis.Among 3047 cases reviewed, 31 matched our inclusion criteria. Initial diagnosis was 20 SH1 fractures, 8 acute ankle sprains, and 3 uncertain, with a change in diagnosis for 48.5% at follow-up.Accurate diagnosis can be difficult in pediatric ankle trauma, with case management and specific treatments varying considerably. This study reinforces the need to evaluate the safety of a general treatment algorithm for all lateral ankle trauma with normal radiographs.Level of evidence III.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".