Essential surgery delivery in the Northern Kivu Province of the Democratic Republic of the Congo
Bibliographic record
Abstract
INTRODUCTION: Surgical services are an essential part of a functional healthcare system, but the Lancet Commission of Global Surgery (LCoGS) indicators of surgical capacity such as perioperative workforce and surgical volume are unknown in many low- and middle-income countries (LMICs) including the Democratic Republic of Congo (DRC). We aimed to determine the surgical capacity and its associated factors within the DRC. METHODS: Hospitals were assessed in the North Kivu province of the DRC. Hospital characteristics and surgical rates were determined using the WHO-PGSSC hospital assessment tool and operating room (OR) registries. The primary outcome of interest was the number of Bellwether operations (i.e. Caesarean sections, laparotomies, and external fixation for bone fractures) per 100,000 people. Univariate and multiple linear regressions were performed. Primary predictors were the number of trained surgeons, anaesthesiologists, and obstetricians (SAOs) and the number of perioperative providers (including clinical officers and nurse anaesthetists) per 100,000 people. RESULTS: Twenty-eight hospitals in North Kivu were assessed over one year in 2021; 24 (86%) were first-level referral health centres while 4 (14%) were second-level referral hospitals. In total, 11,176 Bellwether procedures were performed in the region in one year. Rates per 100,000 people were 1,461 Bellwether surgical interventions, 1.05 SAOs, and 13.1 perioperative providers. In univariate linear regression analysis, each additional SAO added 239 additional cases annually (p = 0.023), while each additional perioperative provider added 110 cases annually (p < 0.001). In our multiple regression analysis adjusting for other hospital services, the association between workforce and Bellwether surgeries was no longer significant. CONCLUSIONS: The surgical workforce in DRC did not meet the LCoGS benchmark of 20 SAOs per 100,000 people but was not an independent predictor of surgical capacity. Major investment is needed to simultaneously bolster healthcare facilities and increase surgical workforce training.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".