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Record W2738256387 · doi:10.1093/tropej/fmx047

Socioeconomic Condition and Prevalence of Malaria Fever in Pakistani Children: Findings from a Community Health Survey

2017· article· en· W2738256387 on OpenAlex
Atta Muhammad Asif, Muhammad Tahir, Irshad Ahmad Arshad

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Tropical Pediatrics · 2017
Typearticle
Languageen
FieldMedicine
TopicMalaria Research and Control
Canadian institutionsMcGill University
Fundersnot available
KeywordsMalariaMedicineSocioeconomic statusDemographyContext (archaeology)Odds ratioLogistic regressionEnvironmental healthPublic healthCross-sectional studyPopulationImmunologyGeographyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: We assessed the prevalence of malarial fever and its association with demographic and socioeconomic factors in children <5 years of age. METHODS: Using the data of Pakistan Demographic and Health Survey (PDHS), the socioeconomic condition (SEC) was assessed by using a household wealth index as a proxy indicator, generated through principal component analysis. Two-stage sampling was used for selection of households, and multilevel logistic regression analysis was performed. RESULTS: The PDHS contains 10 935 children <5 years of age with valid information about malaria fever. In total, 36% (3930) children have malaria 2 weeks before the survey. A decreasing trend in prevalence of malaria fever was found with increasing SEC. Compared with SEC Quintile V, children of SEC Quintile I were more likely to get fever [adjusted odds ratio (AOR)=1.40 (1.15-1.69)] and of SEC Quintile II [AOR = 1.23 (1.03-1.45)]. CONCLUSION: SEC has a significant impact on the prevalence of malaria fever in the context of different regions in Pakistan.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.003
Threshold uncertainty score0.481

Codex and Gemma teacher scores by category

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

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 head0.025
GPT teacher head0.333
Teacher spread0.307 · 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