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Record W4385873954 · doi:10.1080/24745332.2023.2237972

Triple inhaled therapy for asthma in Canada

2023· article· en· W4385873954 on OpenAlexaffabout
Kenneth R. Chapman, Meyer Balter, Sacha Bhinder, Alan Kaplan, Andrew McIvor, Panayiota Papadopoulos, Krystelle Godbout

Bibliographic record

VenueCanadian Journal of Respiratory Critical Care and Sleep Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsInstitut universitaire de cardiologie et de pneumologie de QuébecSt. Joseph’s Healthcare HamiltonThe Scarborough HospitalMount Sinai HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsInhalerMedicineLamaAsthmaIntensive care medicineCombination therapyReferralInternal medicineFamily medicineCOPD

Abstract

fetched live from OpenAlex

A significant number of patients with asthma have poor control on their current inhaled therapies, typically a combination of inhaled corticosteroids (ICS) and long-acting beta-2 adrenergic bronchodilators (LABA). Adding a long-acting antimuscarinic agent (LAMA) has been shown to improve asthma control and the availability of triple therapy formulations (ICS/LABA/LAMA) in a single inhaler device or single inhaler triple therapy (SITT) mitigates the adherence concerns associated with use of multiple inhaler devices. Here, we provide an overview of the pivotal data concerning the use of triple asthma therapy in patients with poor control on ICS-LABA treatment, and present our expert approach to their application in the routine clinical management of such patients as well the appropriate sequencing of initiating triple therapy and seeking a referral for consideration of more advanced therapies.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.217

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.001

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 head0.028
GPT teacher head0.303
Teacher spread0.275 · 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 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

Citations1
Published2023
Admission routes2
Has abstractyes

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