Geospatial Mapping of Pediatric Surgical Capacity in North Kivu, Democratic Republic of Congo
Bibliographic record
Abstract
BACKGROUND: Despite recent attention to the provision of healthcare in low- and middle-income countries, improvements in access to surgical services have been disproportionately lagging. METHODS: This study analyzes the geographic variability in access to pediatric surgical services in the province of North Kivu, Democratic Republic of Congo (DRC). On-site data collection was conducted using the Global Assessment of Pediatric Surgery tool. Spatial distribution of providers was mapped using the Geographical Information System and open-sourced spatial data to determine distances traveled to access surgical care. RESULTS: Forty facilities were evaluated across 32 health zones; 68.9% of the provincial population was within 15 km of these facilities. Eleven facilities met a minimum World Health Organization safety score of 8; 48.1% of the population was within 15 km of corresponding facilities. The majority of children were treated by someone with specific pediatric surgery training in only 4 facilities; one facility had a trained pediatric anesthesia provider. Fifty-seven percent of the population was within 15 km of a facility with critical care and emergency medicine (EM) capabilities. There was one pediatric critical care provider and no pediatric EM providers identified within the province. Location-allocation assessment is needed to combine geographic area with potential for greatest impact and facility assessment. CONCLUSIONS: Limitations in access to surgical care in the DRC are multifactorial with poor resources, few formally trained surgical providers, and near-absent access to pediatric anesthesiologists. The study highlights the deficits in the capacity for surgical care while demonstrating a reproducible model for assessment and identification of ways to improve access to care.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".