Burn management capacity in low and middle-income countries: A systematic review of 458 hospitals across 14 countries
Bibliographic record
Abstract
IMPORTANCE: More than 90% of thermal injury-related deaths occur in low-resource settings. While baseline assessment of burn management capabilities is necessary to guide capacity building strategies, limited data exist from low and middle-income countries (LMICs). OBJECTIVE: The objective of our review is to assess burn management capacity in LMICs. EVIDENCE REVIEW: A PubMed literature review was performed based on studies assessing baseline surgical capacity in individual LMICs. Seven criteria were used to assess burn management capabilities: presence of surgeon, presence of anesthesiologist, basic resuscitation capabilities, acute burn management, management of burn complications, endotracheal intubation and skin grafts. FINDINGS: Fourteen studies were reviewed using data from 458 hospitals in fourteen countries. Of these, 82.3% (284/345) of hospitals had the capacity to provide basic resuscitation and 84.9% (275/324) were capable of providing acute burn management. Endotracheal intubation was only available at 38.3% (51/133) of hospitals. Moreover, only 35.6% (111/312) and 37.9% (120/317) of hospitals were able to provide skin grafts and treat burn complications, respectively. CONCLUSION: Many hospitals in LMICs are capable of initial burn management and basic resuscitation. However, deficiencies still exist in the capacity to systematically provide advanced burn care. Efforts should be made to better document resources in order to guide burn management resource allocation.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".