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OP013 Topic: AS09–Global Health/Resource Limited Setting/Health Inequalities/Impact of Global Warming/Other: ESTIMATE OF PEDIATRIC ACUTE CRITICAL ILLNESS ACROSS RESOURCE-LIMITED SETTINGS: THE GLOBAL PAEDIATRIC ACUTE CRITICAL ILLNESS POINT PREVALENCE STUDY

2024· article· en· W4404042314 on OpenAlexaff
Teresa Kortz, A. Holloway, Asya Agulnik, D. He, Stephanie Gordon Rivera, Qalab Abbas, John Adabie Appiah, Anita V. Arias, J. Attebery, Jhon Camacho‐Cruz, Paula Caporal, Karla Meneses Rodrigues, Ericka L. Fink, N. Kissoon, J.H. Lee, E. López Barón, Srinivas Murthy, Fiona Muttalib, Katie R. Nielsen, Kenneth E. Remy, António Teixeira Rodrigues, Firas Sakaan, A.V. Saint Andre-Vonarnim, William C. Blackwelder, M Wiens, Adnan Bhutta

Bibliographic record

VenuePediatric Critical Care Medicine · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsBC Children's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineCritical illnessIntensive care medicineGlobal healthInequalityIllness severitySeverity of illnessCritically illPublic healthNursing

Abstract

fetched live from OpenAlex

Aims & Objectives: Children in resource-limited settings (RLS) bear a disproportionate burden of mortality. Most life-threatening pediatric illnesses can be managed with basic critical care interventions but, in RLS, are often managed without adequate resources and outside of formal intensive care units. This study aimed to estimate the prevalence and etiology of P-ACI amongst children presenting to RLS hospitals. Methods: This is a prospective, multinational prevalence study of acutely ill or injured children (28 days-14 years) who presented to RLS hospitals. We excluded children with non-acute presentations. We measured prevalence of P-ACI and followed admitted subjects for hospital outcomes. We used descriptive statistics to summarize site- and population-level data by sociodemographic category and multivariable logistic regression to determine whether sociodemographic category was independently associated with P-ACI. Results: The study included 46 sites from 19 countries (N=7457 subjects). P-ACI prevalence was 13% (N=986/7457). In a multivariable model, lower sociodemographic category was associated with P-ACI (adjusted odds ratio 1.9 [95%CI 1.5-2.2]). The most common P-ACI diagnoses were pneumonia (N=152/986 [15%]), sepsis/septic shock (N=102/986 [10%]), and malaria (N=95/986 [10%]). Cohort mortality was 1% (N=68/7457) and most deaths occurred within 48-hours of presentation (N=47/68 [69%]). All-cause mortality (2.5%) and P-ACI prevalence (29%) were highest in the lowest sociodemographic category. Conclusions: P-ACI is common in RLS hospitals and frequently associated with infections that can be managed with basic critical care services. A coordinated global effort is needed to increase high-quality, basic pediatric critical care services in RLS hospitals to prevent mortality and care for children with life-threatening conditions. Keywords: Resource-limited settings, low- and middle-income countries, paediatrics, child health, global health

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)0.001
metaresearch head score (Gemma)0.003
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: none
Teacher disagreement score0.027
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0270.006

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 head0.030
GPT teacher head0.420
Teacher spread0.389 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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