MétaCan
Menu
Back to cohort
Record W4391132769 · doi:10.1186/s12931-024-02665-w

Mucus plugs in the airways of asthmatic subjects and smoking status

2024· article· en· W4391132769 on OpenAlexaff
Camille Audousset, Sana Swaleh, Ron Olivenstein, Motahareh Vameghestahbanati, Miranda Kirby, Alexandre Semionov, Benjamin M. Smith, James G. Martin

Bibliographic record

VenueRespiratory Research · 2024
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsToronto Metropolitan UniversitySt. Michael's HospitalMcGill University Health Centre
Fundersnot available
KeywordsMucusSputumMedicineAirwayAsthmaInternal medicineImmunologyGastroenterologyPathologySurgeryBiologyTuberculosis

Abstract

fetched live from OpenAlex

BACKGROUND: Mucus plugs have been described in the airways of asthmatic subjects, particularly those with associated with type 2 inflammation and sputum eosinophilia. In the current study we addressed the question of whether smoking, neutrophilic inflammation and airway dimensions affected the prevalence of mucus plugs. METHODS: In a cohort of moderate to severe asthmatics (n = 50), including a group of ex-smokers and current smokers, the prevalence of mucus plugs was quantified using a semi-quantitative score based on thoracic computerized tomography. The relationships between mucus score, sputum inflammatory profile and airway architecture were tested according to patient's smoking status. RESULTS: /FVC. Mucus score was significantly correlated with sputum eosinophils. Among former and active smokers, mucus score was correlated with sputum neutrophils. Mucus score was positively associated with FeNO in non-smoking subjects. The lumen dimensions of the main and lobar bronchi were significantly inversely correlated with mucus score. CONCLUSION: Airway mucus plugs could define an asthma phenotype with altered airway architecture and can occur in asthmatic subjects with either neutrophilic or eosinophilic sputum according to their smoking status.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.435
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.092
GPT teacher head0.409
Teacher spread0.317 · 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

Citations9
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueRespiratory ResearchSame topicAsthma and respiratory diseasesFrench-language works237,207