Socioeconomic Factors Are Associated With Trends in Treatment of Pediatric Femoral Shaft Fractures, and Subsequent Implant Removal in New York State
Bibliographic record
Abstract
BACKGROUND: Disparities exist in access to outpatient pediatric orthopaedic care. The purpose of this study was to assess whether disparities also exist in elective pediatric orthopaedic surgical procedures such as implant removal, and to determine which demographic and socioeconomic factors may be associated with differences in treatment. METHODS: Children aged 7 to 18 inclusive who sustained femoral shaft fractures between the years 1997 and 2010 were identified in the New York State SPARCS database. Patient age, sex, race/ethnicity, insurance status, education, and poverty were identified. Factors associated with the method of fracture treatment were assessed through multivariate regression analysis. The subset of patients that received internal fixation were followed up until 2011 inclusive for implant removal. Factors associated with implant removal were assessed using a Cox proportional hazards survival analysis (time to implant removal). RESULTS: Of the 3220 closed femoral shaft fractures identified, 2609 (81%) were treated with internal fixation, 9 (0.3%) had open treatment without implants, 203 (6.3%) were treated with external fixation, and 399 (12.4%) with closed methods. Patients with No Fault/Accident insurance by No Fault/Accident insurance were more likely to undergo internal fixation compared with patients with private insurance (P<0.001). Of the 3220 patients, 2572 were included in the implant removal subanalysis. Implant removal was performed in 725 (28.2%) patients. In the multivariate model, patients were more likely to undergo removal if they were younger (P<0.001), white [vs. black (P<0.001), vs. Hispanic (P=0.035), vs. other (P=0.001)], and lived in neighborhoods with less poverty (P=0.016). Insurance status was not a statistically significant predictor of implant removal. CONCLUSIONS: There is an association between implant removal and younger age, white race, and higher socioeconomic status in children. Awareness of these disparities should prompt further evaluation of causation, whether it be from lack of evidence-based guidelines for implant removal, surgeon bias, variations in reimbursement, or disparities in access to care. Further study is recommended to better elucidate the indications for implant removal in children and the causes for the disparities identified here. LEVEL OF EVIDENCE: Level III-retrospective cohort study.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".