A Survey on Critical Care Resources and Practices in Low- and Middle-Income Countries
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Bibliographic record
Abstract
BACKGROUND: Timely and appropriate care is the key to achieving good outcomes in acutely ill patients, but the effectiveness of critical care may be limited in resource-limited settings. OBJECTIVES: This study sought to understand how to implement best practices in intensive care units (ICU) in low- and middle-income countries (LMIC) and to develop a point-of-care training and decision-support tool. METHODS: An internationally representative group of clinicians performed a 22-item capacity-and-needs assessment survey in a convenience sample of 13 ICU in Eastern Europe (4), Asia (4), Latin America (3), and Africa (2), between April and July 2012. Two ICU were from low-income, 2 from low-middle-income, and 9 from upper-middle-income countries. Clinician respondents were asked about bed capacity, patient characteristics, human resources, available medications and equipment, access to education, and processes of care. RESULTS: Thirteen clinicians from each of 13 hospitals (1 per ICU) responded. Surveyed hospitals had median of 560 (interquartile range [IQR]: 232, 1,200) beds. ICU had a median of 9 (IQR: 7, 12) beds and treated 40 (IQR: 20, 67) patients per month. Many ICU had ≥ 1 staff member with some formal critical care training (n = 9, 69%) or who completed Fundamental Critical Care Support (n = 7, 54%) or Advanced Cardiac Life Support (n = 9, 69%) courses. Only 2 ICU (15%) used any kind of checklists for acute resuscitation. Ten (77%) ICU listed lack of trained staff as the most important barrier to improving the care and outcomes of critically ill patients. CONCLUSIONS: In a convenience sample of 13 ICU from LMIC, specialty-trained staff and standardized processes of care such as checklists are frequently lacking. ICU needs-assessment evaluations should be expanded in LMIC as a global priority, with the goal of creating and evaluating context-appropriate checklists for ICU best practices.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it