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Record W3116299965 · doi:10.1097/ccm.0000000000004769

Pediatric Emergency and Critical Care Resources and Infrastructure in Resource-Limited Settings: A Multicountry Survey*

2020· article· en· W3116299965 on OpenAlexaff
Fiona Muttalib, Sebastián González‐Dambrauskas, Jan Hau Lee, Mardi Steere, Asya Agulnik, Srinivas Murthy, Neill K. J. Adhikari

Bibliographic record

VenueCritical Care Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsSunnybrook HospitalHealth Sciences CentreBC Children's HospitalUniversity of TorontoSunnybrook Health Science CentreHospital for Sick Children
Fundersnot available
KeywordsMedicinePsychological interventionIntensivistIntensive careDescriptive statisticsDeveloping countryFamily medicineEmergency medicineMedical emergencyIntensive care medicineNursingEconomic growth

Abstract

fetched live from OpenAlex

OBJECTIVES: To describe the infrastructure and resources for pediatric emergency and critical care delivery in resource-limited settings worldwide. DESIGN: Cross-sectional survey with survey items developed through literature review and revised following piloting. SETTING: The electronic survey was disseminated internationally in November 2019 via e-mail directories of pediatric intensive care societies and networks and using social media. PATIENTS: Healthcare providers who self-identified as working in resource-limited settings. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Results were summarized using descriptive statistics and resource availability was compared across World Bank country income groups. We received 328 responses (238 hospitals, 60 countries), predominantly in Latin America and Sub-Saharan Africa (n = 161, 67.4%). Hospitals were in low-income (28, 11.7%), middle-income (166, 69.5%), and high-income (44, 18.4%) countries. Across 174 PICU and adult ICU admitting children, there were statistically significant differences in the proportion of hospitals reporting consistent resource availability ("often" or "always") between country income groups (p < 0·05). Resources with limited availability in lower income countries included advanced ventilatory support, invasive and noninvasive monitoring, central venous access, renal replacement therapy, advanced imaging, microbiology, biochemistry, blood products, antibiotics, parenteral nutrition, and analgesic/sedative drugs. Seventy-seven ICUs (52.7%) were staffed 24/7 by a pediatric intensivist or anesthetist. The nurse-to-patient ratio was less than 1:2 in 71 ICUs (49.7%). CONCLUSIONS: Contemporary data demonstrate significant disparity in the availability of essential and advanced human and material resources for the care of critically ill children in resource-limited settings. Minimum standards for essential pediatric emergency and critical care in resource-limited settings are needed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.020
GPT teacher head0.325
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations70
Published2020
Admission routes1
Has abstractyes

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