Surgical Care during Humanitarian Crises: A Systematic Review of Published Surgical Caseload Data from Foreign Medical Teams
Bibliographic record
Abstract
OBJECTIVE: Humanitarian surgery is often organized and delivered with short notice and limited time for developing unique strategies for providing care. While some surgical pathologies can be anticipated by the nature of the crisis, the role of foreign medical teams in treating the existing and unmet burden of surgical disease during crises is unclear. The purpose of this study was to examine published data from crises during the years 1990 through 2011 to understand the role of foreign medical teams in providing surgical care in these settings. METHODS: A literature search was completed using PubMed, MEDLINE, and EMBASE databases to locate relevant manuscripts published in peer-reviewed journals. A qualitative review of the surgical activities reported in the studies was performed. RESULTS: Of 185 papers where humanitarian surgical care was provided by a foreign medical team, only 11 articles met inclusion criteria. The reporting of surgical activities varied significantly, and pooled statistical analysis was not possible. The quality of reporting was notably poor, and produced neither reliable estimates of the pattern of surgical consultations nor data on the epidemiology of the burden of surgical diseases. The qualitative trend analysis revealed that the most frequent procedures were related to soft tissue or orthopedic surgery. Procedures such as caesarean sections, hernia repairs, and appendectomies also were common. As length of deployment increased, the surgical caseload became more reflective of the existing, unmet burden of surgical disease. CONCLUSIONS: This review suggests that where foreign medical teams are indicated and requested, multidisciplinary surgical teams capable of providing a range of emergency and essential surgical, and rehabilitation services are required. Standardization of data collection and reporting tools for surgical care are needed to improve the reporting of surgical epidemiology in crisis-affected populations.
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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 | 0.015 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.030 | 0.030 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".