When Surgical Resources are Severely Constrained, Who Receives Care? Determinants of Access to Orthopaedic Trauma Surgery in Uganda
Bibliographic record
Abstract
BACKGROUND: In low- and middle-income countries, the volume of traumatic injuries requiring orthopaedic intervention routinely exceeds the capacity of available surgical resources. The objective of this study was to identify predictors of surgical care for lower extremity fracture patients at a high-demand, resource-limited public hospital in Uganda. METHODS: Skeletally mature patients admitted with the intention of definitive surgical treatment of an isolated tibia or femur fractures to the national referral hospital in Uganda were recruited to participate in this study. Demographic, socioeconomic, and clinical data were collected through participant interviews at the time of injury and 6 months post-injury. Social capital (use of social networks to gain access to surgery), financial leveraging, and ethnicity were also included as variables in this analysis. A probit estimation model was used to identify independent and interactive predictors of surgical treatment. RESULTS: Of the 64 patients included in the final analysis, the majority of participants were male (83%), with a mean age of 40.6, and were injured in a motor vehicle accident (77%). Due to resource constraints, only 58% of participants received surgical care. The use of social capital and femur fractures were identified as significant predictors of receiving surgical treatment, with social capital emerging as the strongest predictor of access to surgery (p < 0.05). CONCLUSION: Limited infrastructure, trained personnel, and surgical supplies rations access to surgical care. In this environment, participants with advantageous social connections were able to self-advocate for surgery where demand for these services greatly exceeded available resources.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".