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Record W3091436339 · doi:10.24095/hpcdp.30.2.03f

Fardeau de la comorbidité chez les patients asthmatiques traités en Colombie Britannique

2010· article· fr· W3091436339 on OpenAlexvenueaboutno aff
R. Prosser, B. Carleton, A. Smith

Bibliographic record

VenueMaladies chroniques et blessures au Canada · 2010
Typearticle
Languagefr
FieldMedicine
TopicHealth Promotion and Cardiovascular Prevention
Canadian institutionsnot available
Fundersnot available
KeywordsGynecologyMedicineHumanitiesArt

Abstract

fetched live from OpenAlex

<sec> <title>Résumé</title> Jusqu’à maintenant, la prévalence des affections comorbides chez les patients asthmatiques a peu été étudiée. En utilisant des données administratives transversales de 1996-1997 fournies par les services de santé de la Colombie-Britannique , nous avons comparé cette prévalence avec celle de la population adulte en général, grâce à l’aide d’une méthode d’identification des affections comorbides normalisée, le système de classement des maladies Adjusted Clinical Group (ACG). Nous avons également dressé le portrait du fardeau de la comorbidité chez les enfants asthmatiques. Les adultes asthmatiques sont significativement plus à risque de présenter diverses affections comorbides, notamment des infections respiratoires, une rhinite allergique et huit affections chroniques ayant des répercussions importantes et une prévalence élevée (ACRIPE). Un adulte asthmatique sur quatre souffre de dépression, l’ACRIPE la plus fréquente. Le fardeau de la comorbidité est plus faible chez les enfants asthmatiques, mais 12,6 % sont atteints d’une affection chronique, stable ou non, l’ACRIPE la plus fréquente étant aussi la dépression. Le fardeau de la comorbidité chez les adultes asthmatiques est lourd et complexe, en particulier en ce qui a trait aux affections chroniques multiples. Nous traitons des conséquences de ce fardeau sur la planification et la prestation de services de santé. </sec>

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.386
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.284
Teacher spread0.277 · 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.

Study designNot applicable
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

Citations0
Published2010
Admission routes2
Has abstractyes

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