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Does the current stepwise approach to asthma pharmacotherapy encourage over‐treatment?

2010· article· en· W1569955040 on OpenAlexaff
Paul M. O’Byrne, Helen K. Reddel, Gene Colice

Bibliographic record

VenueRespirology · 2010
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare Hamilton
Fundersnot available
KeywordsMedicineAsthmaPharmacotherapyIntensive care medicineAsthma managementClinical PracticePhysical therapyNursingInternal medicine

Abstract

fetched live from OpenAlex

For the past 20 years, asthma pharmacotherapy has been described in clinical practice guidelines in terms of a stepwise approach, with medications and/or doses increased if asthma is not well-controlled, and reduced once good control is achieved and maintained. Although many patients with asthma are untreated, there are also significant problems with over-treatment once regular controller therapy is commenced. This increases the cost of treatment and exposes patients to unnecessary risks of side-effects. The present pro-con debate addresses the question of whether the stepwise approach itself leads to over-treatment. Two asthma experts discuss factors for and against this proposition, identify issues on which more research is needed, and suggest areas in which guidelines can be changed in order to facilitate more appropriate prescribing of asthma medications. These strategies include better validation of the concepts underlying asthma treatment recommendations, stronger recommendations that every treatment change should be followed up with a scheduled review using evidence-based assessment tools and incorporation of phenotype-specific considerations into treatment recommendations. In addition, the process for development and dissemination of clinical practice guidelines should ensure that recommendations are easily understood, feasible to implement, and relevant to everyday asthma care and the needs and concerns of patients and clinicians.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.936
Threshold uncertainty score0.465

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 head0.014
GPT teacher head0.324
Teacher spread0.310 · 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 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

Citations14
Published2010
Admission routes1
Has abstractyes

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