Functional outcome of supracondylar elbow fractures in children: a 3- to 5-year follow-up
Bibliographic record
Abstract
BACKGROUND: Long-term functional outcomes of supracondylar elbow fractures (SCEF) have not been well documented in the literature. We retrospectively evaluated functional outcomes of pediatric SCEF using the Disabilities of the Arm, Shoulder and Hand (DASH) questionnaire. METHODS: We retrospectively reviewed the outcomes of patients who presented to our tertiary care pediatric emergency department with SCEF between January 2005 and December 2009. We reviewed their charts to assess several clinical parameters, including age, sex, Gartland classification of SCEF, weight, comorbidities, treatment intervention, physiotherapy and the extremity involved. The DASH questionnaire was administered in 2012. We performed a multiple linear regression analysis to determine the significance of these clinical parameters as they related to the DASH score for functional outcome. RESULTS: We included 94 patients with SCEF in our review. Pediatric SCEF had good functional outcomes based on the DASH questionnaire (mean score 0.77 ± 2.10). We obtained the following DASH scores: 0.45 ± 2.20 for type I, 1.09 ± 1.70 for type II and 1.43 ± 2.40 for type III fractures. There was no statistical difference in functional outcome, regardless of sex (p = 0.07), age at injury (p = 0.96), fracture type (p = 0.14), weight (p = 0.59), right/left extremity (p = 0.26) or surgery (p = 0.52). CONCLUSION: Our results demonstrate that good functional outcomes can be expected with pediatric SCEF based on the DASH questionnaire, regardless of age at injury, sex, weight, right/left extremity or surgical/nonsurgical intervention, provided satisfactory reduction is achieved and maintained.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".