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Record W2016687680 · doi:10.1080/02770900701815743

The Burden of Illness Experienced by Young Children Associated with Asthma: A Population-Based Cohort Study

2008· article· en· W2016687680 on OpenAlexaffabout
Teresa To, Sharon Dell, Paul T. Dick, Lisa Cicutto

Bibliographic record

VenueJournal of Asthma · 2008
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsInstitute for Clinical Evaluative SciencesSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineAsthmaHealth careCohortPopulationPediatricsCohort studyEnvironmental healthFamily medicineDemographyInternal medicine

Abstract

fetched live from OpenAlex

Using Ontario healthcare administrative databases, over 228,000 children aged 0 to 9 years were identified as having asthma between 1994 and 1998 and followed until they turned 10 years old or the end of the study. The prevalence of childhood asthma increased by 35% during the study period. These children had a higher healthcare utilization and cost over $100 more per child per year than the general population, and contributed to over one third of the total Ontario Health Insurance Plan expenditures. Findings of this study revealed an enormous burden of illness to children with asthma and the healthcare system.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.712
Threshold uncertainty score0.579

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.248
Teacher spread0.242 · 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

Citations39
Published2008
Admission routes2
Has abstractyes

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