A survey on the resources and practices in pediatric critical care of resource-rich and resource-limited countries
Bibliographic record
Abstract
BACKGROUND: Contemporary critical care research necessitates involvement of multiple centers, preferably from many countries. Adult and pediatric research networks have produced outstanding data; however, their involvement is restricted to a small percentage of the industrialized nations. Implementation of their findings in low- and middle-income countries (LMICs) is fraught with challenges. METHODS: We conducted an online international survey to assess and compare disease burden and resources to participate in multicenter research studies through a listserv of the World Federation of Pediatric Intensive and Critical Care Societies. Respondents were grouped into high-income countries and LMICs on the basis of World Bank classification. RESULTS: Survey was completed by 73 centers in 34 countries (34 from high-income countries and 39 from LMICs). Compared with high-income countries, the pediatric intensive care units in LMICs were characterized by a lower number of critical care specialists, more difficult access to hemodialysis, and a lower number of elective postoperative patients, but a similar overall disease burden. Training and resources for research were comparable in the two cohorts. CONCLUSIONS: Although differences exist in access to both trained providers and equipment, the survey results were more striking in their similarity. It is essential that centers from LMICs be included in multinational studies, to generate results applicable to all children worldwide.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".