Access to Limited Critical Care and Risk of Mortality in Rwanda: A Prospective Cohort Study
Bibliographic record
Abstract
IMPORTANCE: There is a large discrepancy between need and access to critical care in low- and middle-income countries. Little is known about what subgroups of patients are being prioritized for critical care. OBJECTIVES: The primary objective was to assess what clinical, demographic, and socioeconomic variables were associated with timely ICU admission. Secondary objectives included determining the rate of ICU admission among patients who met admission criteria, inpatient mortality, and length of stay. DESIGN: Prospective cohort study. SETTING AND PARTICIPANTS: All adult patients meeting ICU admission criteria at the University Teaching Hospital of Butare, Huye, Rwanda. MAIN OUTCOMES AND MEASURES: The primary outcome was the proportion of patients admitted to ICU within 24 hours of being identified as critically ill. A multivariable logistic regression model was used to assess whether clinical, demographic, or socioeconomic factors are associated with timely ICU admission. Secondary outcomes were the proportion of patients admitted to ICU at any time, inpatient mortality, and length of stay. RESULTS: Three hundred eighteen patients were enrolled between January 24, 2024, and June 3, 2024. Eighty-eight (27.7%) were admitted to ICU within 24 hours. Requiring ICU for postoperative recovery (odds ratio [OR], 8.21; 95% CI, 3.64-19.8), obstetric patients (OR, 2.43; 95% CI, 0.92-6.41), and ICU bed availability (OR, 1.26; 95% CI, 1.02-1.55) increased the odds of timely ICU admission in multivariable analysis. Socioeconomic status, gender, and social connections had minimal association with ICU admission, with wide CIs. The inpatient mortality rate was 44.0% and average length of stay was 14 days. CONCLUSIONS AND RELEVANCE: Obstetric and postoperative patients are prioritized for ICU admission. There is a large unmet need for critical care in Rwanda, and mortality among critically ill patients is high.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".