Surgery for Conditions of Infectious Etiology in Resource-Limited Countries Affected by Crisis: The Médecins Sans Frontières Operations Centre Brussels Experience
Bibliographic record
Abstract
BACKGROUND: Surgery for infection represents a substantial, although undefined, disease burden in low- and middle-income countries (LMICs). Médecins Sans Frontières-Operations Centre Brussels (MSF-OCB) provides surgical care in LMICs and collects data useful for describing operative epidemiology of surgical need otherwise unmet by national health services. This study aimed to describe the experience of MSF-OCB operations for infections in LMICs. By doing so, the results might aid effective resource allocation and preparation of future humanitarian staff. METHODS: Procedures performed in operating rooms at facilities run by MSF-OCB from July 2008 through June 2014 were reviewed. Projects providing specialty care only were excluded. Procedures for infection were described and related to demographics and reason for humanitarian response. RESULTS: A total of 96,239 operations were performed at 27 MSF-OCB sites in 15 countries between 2008 and 2014. Of the 61,177 general operations, 7,762 (13%) were for infections. Operations for skin and soft tissue infections were the most common (64%), followed by intra-abdominal (26%), orthopedic (6%), and tropical infections (3%). The proportion of operations for skin and soft tissue infections was highest during natural disaster missions (p<0.001), intra-abdominal infections during hospital support missions (p<0.001) and orthopedic infections during conflict missions (p<0.001). CONCLUSION: Surgical infections are common causes for operation in LMICs, particularly during crisis. This study found that infections require greater than expected surgical input given frequent need for serial operations to overcome contextual challenges and those associated with limited resources in other areas (e.g., ward care). Furthermore, these results demonstrate that the pattern of operations for infections is related to nature of the crisis. Incorporating training into humanitarian preparation (e.g., surgical sepsis care, ultrasound-guided drainage procedures) and ensuring adequate resources for the care of surgical infections are necessary components for providing essential surgical care during crisis.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".