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Record W5620722 · doi:10.1093/pch/17.7.e46

Long-term management of asthma in First Nations and Inuit children: A knowledge translation tool based on Canadian paediatric asthma guidelines, intended for use by front-line health care professionals working in isolated communities

2012· article· en· W5620722 on OpenAlexaffabout
B. Louise Giles, Hans Pasterkamp

Bibliographic record

VenuePaediatrics & Child Health · 2012
Typearticle
Languageen
FieldHealth Professions
TopicPediatric health and respiratory diseases
Canadian institutionsUniversity of ManitobaChildren's Hospital of WinnipegChildren's Hospital of Eastern OntarioUniversity of Ottawa
Fundersnot available
KeywordsFront lineAsthmaKnowledge translationMedicineTerm (time)Front (military)Family medicineHealth professionalsPediatricsHealth careNursingPolitical scienceGeographyKnowledge managementComputer science

Abstract

fetched live from OpenAlex

Asthma is a serious health problem for First Nations and Inuit children. In children younger than one year of age, asthma needs to be distinguished from viral bronchiolitis, which is unusually common in Canadian Aboriginal children. In children younger than six years of age, the diagnosis depends on the presence of typical symptoms, the absence of atypical features and the documentation of response to therapy - particularly a rapid, transient response to bronchodilators. In older children, the presence of reversible airway obstruction should be determined using spirometry whenever feasible to confirm the diagnosis. Environmental triggers should be evaluated and corrected whenever possible. Regular use of inhaled steroids is the most important measure for maintaining good asthma control in children with asthma. Clients and their families should receive asthma education. Control should be regularly reassessed at follow-up visits in health centres, with therapy adjusted to the lowest level capable of maintaining good control.

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)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.871
Threshold uncertainty score0.256

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.072
GPT teacher head0.384
Teacher spread0.312 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations9
Published2012
Admission routes2
Has abstractyes

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