Prehospital Extremity Fracture Management in Low and Middle‐Income Countries: A Scoping Review of Lay First Responders and Traditional Bonesetters
Bibliographic record
Abstract
PURPOSE: Low- and middle-income countries (LMICs) experience the highest rates of injury-related deaths globally, exacerbated by a lack of robust emergency medical services (EMS). Though fractures contribute substantially to global injury, little is known about prehospital management of extremity fractures in LMICs. METHODS: This review included literature published between January 2000 and January 2024. Inclusion criteria pertained to prehospital settings, defined as care rendered prior to hospital presentation, including care provided by lay first responders (LFRs), professional EMS personnel, and traditional bonesetters (TBS). Multiple authors used the Newcastle-Ottawa scale to assess texts meeting inclusion criteria, extracting relevant details for analysis. RESULTS: Of 1251 articles identified, 25 met inclusion criteria. Studies spanned 9 countries across 4 continents, with 14 articles studying care by TBS, 9 by LFRs, and 2 by other prehospital providers. LFR training courses report a combined weighted average pre-/post-course difference of 29.16 percentage points. A total of 67% of included studies report adverse outcomes associated with TBS-managed fractures in the prehospital setting. TBS care is often sought prior to hospital presentation due to sociocultural beliefs, accessibility, and cheaper costs. Few training courses for TBS have been performed, though one course reports a 20.4% increase in fracture management knowledge. CONCLUSION: In certain resource-limited settings, TBS provide most initial fracture management, which may adversely impact outcomes. Knowledge transfer has been demonstrated during prehospital fracture management courses for LFRs and TBS. Early evidence suggests TBS training and integration into healthcare systems may reduce complication rates, improving long-term outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".