Critical care resources, disaster preparedness, and sepsis management: Survey results from the Asia Pacific region
Bibliographic record
Abstract
There is paucity of data on critical care resources, disaster preparedness, and sepsis management in countries within the Asia Pacific region. An online survey was conducted from 15 April to 17 July 2020. Snowball sampling through the Asia Pacific Sepsis Alliance and network contacts was used to recruit respondents. Countries were grouped according to the World Bank Country Income 2019 classification into lower-middle income (LMIC), upper-middle income (UMIC), and high-income (HIC). Survey questions addressed to hospital characteristics, critical care resources, disaster preparedness, and sepsis management. In total, 59 hospitals from 15 countries responded (33 LMICs, 8 UMICs, 18 HICs) with most responses from the Philippines (10; 16.9%). Median [Inter-quartile range (IQR)] hospital and Intensive Care Unit (ICU) bed capacity was 798 (500–1,001) and 37 (19–59), respectively. Median (IQR) doctor-to-patient and nurse-to-patient day ratios were 1:5 (1:3–1:8) and 1:2 (1:1–1:2), respectively. Availability of 24/7 physiotherapy services, 24/7 Medical resonance Imaging (MRI), point-of-care lactate, and “reserve” antibiotics was limited. Most ICUs had a disaster management plan (88%) and access to Personal Protective Equipment (96%). The most commonly adopted sepsis guideline was the Surviving Sepsis Campaign guidelines (77%). LMIC/UMIC ICUs had lower nurse-to patient ratio and surge capacity along with limited access to 24/7 physiotherapy and MRI services, and interventions like Extra Corporeal Membrane Oxygenation, and Continuous Renal Replacement Therapy. Self-reported adoption and adherence to sepsis guidelines was higher in LMICs/UMICs than HICs. In the Asia Pacific region, critical care resources, disaster preparedness and management of sepsis vary considerably between countries across different income categories. In particular, low surge and isolation capacity in LMICs highlights the need for better health service planning and preparation.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".