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Record W2507173059 · doi:10.1002/oby.21568

Discrepancies between asthma control criteria in asthmatic patients with and without obesity

2016· article· en· W2507173059 on OpenAlexaff
Antoine Vermette, Marie‐Ève Boulay, Louis‐Philippe Boulet

Bibliographic record

VenueObesity · 2016
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsInstitut universitaire de cardiologie et de pneumologie de Québec
Fundersnot available
KeywordsAsthmaMedicineObesityPediatricsPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To compare the prevalence of discrepancies between clinical, physiological, and inflammatory asthma control parameters between patients with asthma and obesity and patients with asthma but not obesity using the Asthma Control Scoring System (ACSS). METHODS: A retrospective analysis of demographic data and ACSS scores was performed in two groups of patients with asthma (74 with obesity and 74 without obesity) paired for sex, age, and asthma severity. Scores from each asthma control parameter-clinical (respiratory symptoms), physiological (forced expiratory volume in 1 s), and inflammatory (sputum eosinophil percentage)-were compared. Discrepancy was defined as a >20% difference between two scores. RESULTS: The prevalence of discrepancies between scores was similar between asthma patients with or without obesity. A sub-analysis on patients with uncontrolled asthma (ACSS global score <80%) showed a higher prevalence of discrepancies between the clinical and physiological scores in subjects with obesity, the clinical score being higher than the physiological one in most (87%) cases. CONCLUSIONS: Subjects with obesity and uncontrolled asthma show higher clinical scores than physiological scores, suggesting an under-evaluation of asthma symptoms. Future studies are needed to evaluate the influence of obesity on each type of asthma symptom.

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 score0.006
Threshold uncertainty score0.402

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 head0.007
GPT teacher head0.245
Teacher spread0.238 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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