The state of emergency care in Democratic Republic of Congo
Bibliographic record
Abstract
The Democratic Republic of Congo (DRC) is the second largest country on the African continent with a population of over 70 million. It is also a major crossroad through Africa as it borders nine countries. Unfortunately, the DRC has experienced recurrent political and social instability throughout its history and active fighting is still prevalent today. At least two decades of conflict have devastated the civilian population and collapsed healthcare infrastructure. Life expectancy is low and government expenditure on health per capita remains one of the lowest in the world. Emergency Medicine has not been established as a specialty in the DRC. While the vast majority of hospitals have emergency rooms or salle des urgences , this designation has no agreed upon format and is rarely staffed by doctors or nurses trained in emergency care. Presenting complaints include general and obstetric surgical emergencies as well as respiratory and diarrhoeal illnesses. Most patients present late, in advanced stages of disease or with extreme morbidity, so mortality is high. Epidemics include HIV, cholera , measles , meningitis and other diarrhoeal and respiratory illnesses. Lack of training, lack of equipment and fee-for-service are cited as barriers to care. Pre-hospital care is also not an established specialty. New initiatives to improve emergency care include training Congolese physicians in emergency medicine residencies and medic ranger training within national parks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".