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Record W7133051668

Identifying Targets for Improving Quality of Pediatric Emergency and Critical Care in Low-Resource Settings

2023· dissertation· W7133051668 on OpenAlexfundno aff
Fiona Muttalib

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsPsychological interventionEpidemiologyOddsEmergency departmentCause of deathMedical diagnosisDiseaseChild mortalityOdds ratio
DOInot available

Abstract

fetched live from OpenAlex

Introduction: Nearly 6 million children and adolescents died worldwide in 2021, largely due to preventable causes and disproportionately in low- and middle-income countries (LMICs). To reduce child mortality in LMICs, targeted pediatric emergency and critical care (PECC) quality-of-care interventions are needed. Several knowledge gaps must be addressed to inform their design. Objectives: This thesis (1) describes the epidemiology of disease and outcomes of children 0–14 years presenting with acute illness to a District Headquarter (DHQ) hospital in Pakistan, (2) synthesizes the evidence for performance of pediatric mortality prognostic models developed and validated in LMICs, and (3) describes the global infrastructure for PECC in low-resource settings. Synthesis: In Chapter 3, we included 3850 children presenting to Sanghar DHQ hospital outpatient department and 1286 admitted children. Communicable diseases were the most common presenting diagnoses among outpatients and among inpatients 1–9 years. Non-communicable diseases and nutritional disorders were more common with increasing age. At 28 days from hospital admission, 50 children had died. Age <28 days was associated with increased odds of death (OR 4.39 [95% CI 2.4–8.26], p<0.001, reference age: 28 days-14 years). No sex differences in mortality risk were observed. Leading causes of death included neonatal sepsis/meningitis, neonatal encephalopathy, and lower respiratory tract infections. In Chapter 4, we identified seven prognostic models for hospital mortality validated in >1 cohort. The Lambarene Organ Dysfunction Score [0.85 (0.63–0.95)] and Signs of Inflammation in Children that Kill [0.85 (0.84–0.86)] had the highest summary C-statistics (95% CI). All models were at high risk of bias. In Chapter 5, we described infrastructure for PECC in 238 hospitals in 60 countries, predominantly in Latin America and Africa (N=161, 67.4%). Across 174 intensive care units (ICUs), there were statistically significant differences in the proportion of hospitals reporting consistent resource availability between World Bank country income groups. Resources with low availability in low- and lower-middle income countries included specialized staff, ICU equipment, diagnostic tests (imaging, microbiology, biochemistry), and commonly used drugs. Conclusion: This thesis addresses important knowledge gaps regarding the foundations for delivery of high-quality PECC to reduce child mortality from acute illness.

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)0.009
metaresearch head score (Gemma)0.028
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.028
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.095
GPT teacher head0.461
Teacher spread0.366 · 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
Published2023
Admission routes1
Has abstractyes

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