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Record W1974922993 · doi:10.1002/ppul.10312

Childhood wheezing syndromes and healthcare data

2003· article· en· W1974922993 on OpenAlexaff
Anita L. Kozyrskyj, Cameron Mustard, Allan B. Becker

Bibliographic record

VenuePediatric Pulmonology · 2003
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsUniversity of TorontoInstitute for Work & HealthUniversity of ManitobaManitoba Health
Fundersnot available
KeywordsMedicineAsthmaBronchitisHealth carePediatricsCohortAllergyPopulationCohort studyIntensive care medicineEnvironmental healthInternal medicineImmunology

Abstract

fetched live from OpenAlex

There is convincing evidence that several distinct wheezing syndromes exist in childhood. The purpose of this research was to assess the potential of using healthcare utilization profiles to identify wheezing syndromes in children which are distinct from asthma. Using population-based healthcare administrative data, a cohort of children, aged 5-15 years, with bronchitis diagnoses from time of birth to 1995, but no physician diagnoses of asthma, was followed over the period January 1996-March 1998. In this follow-up period, 13% had subsequent healthcare utilization for asthma, 23% had continued healthcare utilization for bronchitis, and 64% had no further healthcare utilization. The likelihood of bronchitis vs. asthma outcomes was determined for a variety of asthma risk factors. In a cohort of 11,043 children with initial healthcare contact for bronchitis but not asthma, two potentially distinct entities of bronchitis emerged from our data: 1) transient bronchitis, similar to transient wheezing of early childhood, which was associated with winter-only healthcare utilization and absence of allergy, and 2) recurrent bronchitis which differed from asthma on the basis of winter-only healthcare utilization, prematurity at birth, absence of allergy, and low socioeconomic status. Healthcare administrative records can be used to describe the natural history of wheezing in children and to identify markers which may discriminate asthma from other syndromes.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.064
Threshold uncertainty score0.425

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.030
GPT teacher head0.294
Teacher spread0.263 · 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

Citations20
Published2003
Admission routes1
Has abstractyes

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