The Feasibility of a Randomized Controlled Trial for Open Tibial Fractures at a Regional Hospital in Uganda
Bibliographic record
Abstract
BACKGROUND: The inclusion of low and middle-income country (LMIC) hospitals in multicenter orthopaedic trials expands the pool of eligible patients and improves the external validity of the evidence. Furthermore, promoting studies in LMIC hospitals defines the optimal treatments for low-resource settings, the conditions under which the majority of musculoskeletal injuries are treated. The objective of this study was to determine the feasibility of a randomized controlled trial comparing external fixation with intramedullary (IM) nailing in patients with an isolated open tibial fracture who presented to a regional hospital in Uganda. METHODS: From July 2016 to July 2017, skeletally mature patients who presented to a Ugandan regional hospital with an isolated Gustilo-Anderson type-II or IIIA open fracture of the tibial shaft were eligible for inclusion. The primary feasibility outcomes were the enrollment rate, the recruitment rate, and the 3 and 12-month follow-up rates. The secondary outcomes included a comparison of 3 and 12-month follow-up rates between the treatment arms and a qualitative assessment of barriers to enrollment, timely treatment, and missed follow-up. RESULTS: During the 12-month enrollment period, 37.5% (30 of 80) of eligible patients were successfully enrolled and operatively treated on the basis of their random allocation, with an enrollment rate of 2.5 patients per month. Of the 30 enrolled patients, 53% completed their 3-month follow-up appointment, and 40% completed their 1-year follow-up appointment. Rates of 1-year follow-up were significantly higher for patients receiving IM nails than for those receiving external fixation (absolute difference, 52%; 95% confidence interval [CI], 21 to 83, p < 0.01). The main reasons that patients declined to participate in the trial were preferences for treatment by traditional bonesetters and prehospital delays that were related to a disorganized referral system. Barriers to follow-up included prohibitive transportation costs and community pressure to turn to traditional forms of treatment. CONCLUSIONS: A regional hospital in Uganda can successfully enroll, randomize, and operatively treat multiple patients with an open tibial fracture each month. Patient follow-up presents substantial concerns over trial feasibility in this setting. Cultural pressure to utilize traditional treatments remains a particularly common barrier to study-participant enrollment and retention.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".