Structure, Organization, and Delivery of Critical Care in Asian ICUs*
Bibliographic record
Abstract
OBJECTIVES: Despite being the epicenter of recent pandemics, little is known about critical care in Asia. Our objective was to describe the structure, organization, and delivery in Asian ICUs. DESIGN: A web-based survey with the following domains: hospital organizational characteristics, ICU organizational characteristics, staffing, procedures and therapies available in the ICU and written protocols and policies. SETTING: ICUs from 20 Asian countries from April 2013 to January 2014. Countries were divided into low-, middle-, and high-income based on the 2011 World Bank Classification. SUBJECTS: ICU directors or representatives. MEASUREMENTS AND MAIN RESULTS: Of 672 representatives, 335 (50%) responded. The average number of hospital beds was 973 (SE of the mean [SEM], 271) with 9% (SEM, 3%) being ICU beds. In the index ICUs, the average number of beds was 21 (SEM, 3), of single rooms 8 (SEM, 2), of negative-pressure rooms 3 (SEM, 1), and of board-certified intensivists 7 (SEM, 3). Most ICUs (65%) functioned as closed units. The nurse-to-patient ratio was 1:1 or 1:2 in most ICUs (84%). On multivariable analysis, single rooms were less likely in low-income countries (p = 0.01) and nonreferral hospitals (p = 0.01); negative-pressure rooms were less likely in private hospitals (p = 0.03) and low-income countries (p = 0.005); 1:1 nurse-to-patient ratio was lower in private hospitals (p = 0.005); board-certified intensivists were less common in low-income countries (p < 0.0001) and closed ICUs were less likely in private (p = 0.02) and smaller hospitals (p < 0.001). CONCLUSIONS: This survey highlights considerable variation in critical care structure, organization, and delivery in Asia, which was related to hospital funding source and size, and country income. The lack of single and negative-pressure rooms in many Asian ICUs should be addressed before any future pandemic of severe respiratory illness.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".