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Record W3013408280 · doi:10.3390/jcm9040921

Effective Asthma Management: Is It Time to Let the AIR out of SABA?

2020· review· en· W3013408280 on OpenAlexaff
Alan Kaplan, Patrick Mitchell, Andrew Cave, Rémi Gagnon, Vanessa Foran, Anne K. Ellis

Bibliographic record

VenueJournal of Clinical Medicine · 2020
Typereview
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsQueen's UniversityUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsMedicineAsthmaAsthma managementFamily medicineInternal medicine

Abstract

fetched live from OpenAlex

For years, standard asthma treatment has included short acting beta agonists (SABA), including as monotherapy in patients with mild asthma symptoms. In the Global Initiative for Asthma 2019 strategy for the management of asthma, the authors recommended a significant departure from the traditional treatments. Short acting beta agonists (SABAs) are no longer recommended as the preferred reliever for patients when they are symptomatic and should not be used at all as monotherapy because of significant safety concerns and poor outcomes. Instead, the more appropriate course is the use of a combined inhaled corticosteroid-fast acting beta agonist as a reliever. This paper discusses the issues associated with the use of SABA, the reasons that patients over-use SABA, difficulties that can be expected in overcoming SABA over-reliance in patients, and our evolving understanding of the use of "anti-inflammatory relievers" in our patients with asthma.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.659
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.087
GPT teacher head0.473
Teacher spread0.386 · 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
GenreReview

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

Citations62
Published2020
Admission routes1
Has abstractyes

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