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Record W1982147076 · doi:10.3402/ijch.v63i1.17649

Risk factors for hospitalization and infection in Canadian Inuit infants over the first year of life - a pilot study

2004· article· en· W1982147076 on OpenAlexaffabout
Alison L. Jenkins, Theresa W. Gyorkos, Lawrence Joseph, Kate N Culman, Brian J. Ward, Gary Pekeles, Elaine L. Mills

Bibliographic record

VenueInternational Journal of Circumpolar Health · 2004
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsMontreal Children's HospitalMontreal General HospitalMcGill University
Fundersnot available
KeywordsMedicineDemographyPediatricsGerontologyEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVES: Inuit infants experience higher mortality and poorer health than other Canadian infants, and suffer disproportionately from bacterial and viral infections. A wide range of inter-related factors affect their health and susceptibility to infection. The objective of the study was to describe hospitalization and morbidity patterns in a cohort of 46 healthy Inuit infants from Iqaluit, Nunavut, over their first year of life. STUDY DESIGN: Risk factors for hospitalization and infections were assessed using multiple linear regression. RESULTS: Infants experienced an average of four respiratory tract infections (RTIs) annually, which accounted for half of the hospitalizations in the cohort. Some interesting trends were evident from the assessment of risk factors using multiple linear regression. Adoption was associated with adverse health effects in addition to those that would be expected due to lack of breast-freeding alone; among infants who were not breast-fed, adopted infants had three more RTIs per year than non-adopted infants. CONCLUSION: The results of this pilot study provide support for undertaking larger epidemiological studies in order to clarify the role of these risk factors, so that future preventive efforts can be informed and effective.

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 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 score0.187
Threshold uncertainty score0.554

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.029
GPT teacher head0.366
Teacher spread0.337 · 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 teacher head, 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

Citations28
Published2004
Admission routes2
Has abstractyes

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