Variability in the Follow-up Management of Pediatric Femoral Fractures
Bibliographic record
Abstract
INTRODUCTION: Variability in follow-up has previously been identified in orthopaedic trauma. Variability in follow-up for pediatric femur fractures has not previously been documented. The aim of this study was to document the variability in clinical and radiographic follow-up for pediatric femur fractures based on the fixation method and the treating surgeon. METHODS: This retrospective case series identified isolated femoral fractures in patients younger than 18 years, treated by eight surgeons at a single center from 2010 to 2015. The total number and frequency of clinical visits, radiographic visits and discrete radiograph views, demographic data, fracture classification, treatment method, and presence of complications were extracted. Variability in follow-up was assessed through descriptive statistics and linear and Poisson regression models. RESULTS: One hundred sixty-four femoral fractures in 160 patients were included. Fractures were stratified by the treating surgeon. The mean length of follow-up ranged from 6.5 to 13.6 months. Complications increased follow-up time by mean 1.7 months (1.3 to 2.4). Patients who were treated with rigid locking nails were followed for the shortest amount of time, averaging 9.9 months, while traction followed by rigid locking nails averaged 24.4 (0.5 to 9.3) months of follow-up. DISCUSSION: Variation in the length of follow-up was identified and was associated with the fixation method and the treating surgeon. Few patients were followed long enough to definitively identify complications and sequelae known to occur after femur fractures such as femoral overgrowth or growth arrest. The results of this study indicate a need for additional study and consensus on an appropriate follow-up for pediatric femur fractures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".