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Record W4403242441 · doi:10.1016/j.chest.2024.09.031

Peak Inspiratory Flow and Inhaler Prescription Strategies in a Specialized COPD Clinical Program

2024· article· en· W4403242441 on OpenAlexaff
Sarah Pankovitch, Michael Frohlich, Bader Alothman, Jeffrey Marciniuk, Joanie Bernier, Dorcas Paul-Emile, Jean Bourbeau, Bryan Ross

Bibliographic record

VenueCHEST Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsInhalerObservational studyCOPDMedical prescriptionMedicineIntensive care medicinePhysical therapyMedical physicsInternal medicineAsthmaNursing

Abstract

fetched live from OpenAlex

Background COPD inhaler regimens should be appropriate for the patient's peak inspiratory flow (PIF) and should ideally consist of single or similar device(s). Research Questions In a subspecialized COPD clinic: (1) What is the prevalence of patients with suboptimal PIF and with inappropriate device(s) for measured PIF? (2) Are there patient-related risk factors associated with suboptimal PIF? (3) What is the prevalence of patients with non-single inhaler therapy (SIT)/nonsimilar devices? (4) Does point-of-care PIF affect clinical decision-making? Study Design and Methods In this single-center real-world observational study, PIF was measured systematically at every outpatient visit in a subspecialized COPD clinic, and point-of-care results were provided to the clinician. Coprimary outcomes were the prevalence of outpatients with suboptimal PIF and with inappropriate devices for measured PIF. Secondary outcomes were patient-related risk factors associated with suboptimal PIF, the prevalence of non-SIT/nonsimilar devices, the prevalence of regimens consisting of either inappropriate device(s) for measured PIF and/or non-SIT/nonsimilar devices, and the effect of point-of-care PIF on clinical decision-making. Results Suboptimal PIF was identified in 45 of 161 participants (28%), and inappropriate device(s) for measured PIF were identified in 18 participants (11.2%). Significant associations were observed between suboptimal PIF and age (1.09; 95% CI, 1.04-1.15), female sex (10.30; 95% CI, 4.45-27.10), height (0.92; 95% CI, 0.88-0.96), BMI (0.90; 95% CI, 0.84-0.96), and FEV 1 (0.09; 95% CI, 0.03-0.26). After adjustment for age and sex, the association between suboptimal PIF and BMI, but not height, remained significant. Non-SIT and/or nonsimilar devices were identified in 50 participants (31.1%). Regimens consisting of either inappropriate device(s) for measured PIF and/or non-SIT/nonsimilar devices were observed in 59 participants (36.6%). Inhaler prescription changes were observed in this latter group (3.39; 95% CI, 1.76-6.64), as well as in patients with suboptimal PIF who already had SIT/similar regimens (2.93; 95% CI, 1.07-7.92). Interpretation Suboptimal PIF and inappropriate devices for measured PIF were highly prevalent among outpatients from a subspecialized COPD clinic. Our results show that female sex, reduced FEV 1 , and low BMI are important, readily identifiable risk factors for suboptimal PIF, and point-of-care PIF can inform clinical decision-making.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.050
GPT teacher head0.369
Teacher spread0.318 · 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

Citations9
Published2024
Admission routes1
Has abstractno

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