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Record W2771698272 · doi:10.1089/jamp.2017.1409

Preferences and Inhalation Techniques for Inhaler Devices Used by Patients with Chronic Obstructive Pulmonary Disease

2017· article· en· W2771698272 on OpenAlexaff
Roxanne Bournival, Raphaël Coutu, Nicolas Goettel, Chun Dian Yang, Antoine Cantin-Lafleur, Catherine M. Lemieux, Anne-Marie Lévesque, Jean-François Patenaude-Monette, François-Olivier Roy, Lucie Blais, Fanny Arbour, A. Fillion, Marie-France Beauchesne

Bibliographic record

VenueJournal of Aerosol Medicine and Pulmonary Drug Delivery · 2017
Typearticle
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalAstraZeneca (Canada)Hôpital du Sacré-Cœur de MontréalUniversité de MontréalCentre Hospitalier Universitaire de Sherbrooke
Fundersnot available
KeywordsInhalerPulmonary diseaseInhalationMedicineIntensive care medicineCOPDNebulizerDiseaseAsthmaAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Inhaler technique and patient preferences are often overlooked when selecting maintenance treatments for patients with chronic obstructive pulmonary disease (COPD), but are important issues in ensuring drug efficacy and patient adherence. Few data on these issues are available for new inhalation devices. OBJECTIVES: inhalation devices, and patient preferences for the three latter inhalers that were recently developed. METHODS: A prospective two-center cross-sectional study of COPD patients was conducted. The patients were required to be current HandiHaler users who had not previously used the new inhalers (Breezhaler, Genuair, Respimat). The patients were given the new devices and asked to identify the one they preferred before and after using the inhaler. Each patient tried the HandiHaler and two devices out of the three new inhalers: one preferred by the patient and one imposed by the investigator. Their inhalation technique was evaluated using an assessment checklist. A logistic regression model was used to determine which device was used with the fewest errors. RESULTS: Of the 98 patients who completed the study, 57.1% (95% CI: 47.4-66.9) had an adequate HandiHaler technique. There was no difference between the proportions of patients with an adequate Breezhaler and Genuair inhalation technique (aOR 1.08, 95% CI: 0.51-2.30), but 62% fewer patients using Respimat had an adequate technique than those using Genuair (aOR for adequate technique 0.38, 95% CI: 0.18-0.82). There were no significant differences in the initial patient preferences for the three new inhalers, and no association between the patient's preference and an adequate inhaler technique. CONCLUSION: Inhalation techniques were suboptimal and varied between inhalers. The arrival of new inhalers is an opportunity to reassess patient techniques and preferences. Further studies should also explore the association between the inhaler preferences and treatment adherence of patients.

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)0.002
metaresearch head score (Gemma)0.009
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 score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.266
Teacher spread0.248 · 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".

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Citations12
Published2017
Admission routes1
Has abstractyes

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