Pediatric Forefoot Fractures
Bibliographic record
Abstract
BACKGROUND: Forefoot fractures account for 6% to 10% of fractures in children, and although the majority heals with supportive treatment, complications may lead to pain and disability. No previous study in children has evaluated complication risk in the emergency department based on initial assessment characteristics. STUDY OBJECTIVES: The study aim was to identify the radiological and clinical variables that increase the complication rate of pediatric forefoot fractures. This may help emergency physicians refer patients who require more thorough follow-up or surgical intervention. METHODS: We evaluated 497 forefoot fractures on initial presentation to a pediatric emergency department at the Children's Hospital at London Health Science Centre over a 6-year period. We collected variables such as degree of angulation, displacement, number of concurrent fractures, and demographic data such as age and sex. We then determined the variables associated with complications by reviewing each patient's chart. RESULTS: Overall, there was a 6.4% complication rate. Analysis identified sex as an important predictor of complications. Females, although representing approximately one third of the sample, represented nearly two thirds of the cases with complicated outcomes (P = 0.001; odds ratio [OR], 4.67). Increased number of fractures was also significant (P = 0.01; OR, 2.41) as was increasing age (P = 0.01; OR, 1.17) and patients who chose to return to the emergency department (P < 0.05; OR, 5.282). Lateral angulation/displacement and anteroposterior angulation/displacement were not associated with increased complications. CONCLUSION: Identifying features, such as female sex, increasing age, multiple fractures, and return to emergency departments for repeat visits, may help guide the emergency physician on whom to refer for specialized care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".