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Record W7015829326

Treatment decisions: insights from people with asthma or COPD and their healthcare providers on disease and treatment decisions

2025· article· en· W7015829326 on OpenAlexaff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2025
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsGlobal Affairs Canada
Fundersnot available
KeywordsAsthmaGeneral partnershipDiseaseCOPDHealth careMental healthFoundation (evidence)Lung diseaseAlternative medicine
DOInot available

Abstract

fetched live from OpenAlex

Ruth Tal-Singer1, Miguel Román-Rodríguez2,3, Ilona McMullan4, Michelle Warner4, Christopher Compton4, Jean Orlow5, MeiLan K Han6 1Global Allergy and Airways Patient Platform, Vienna, Austria; 2Centro de Salud Dra. Teresa Pique, Mallorca, Spain; 3Instituto de Investigación Sanitaria de las Islas Baleares (IdISBa), Mallorca, Spain; 4GSK, London, UK; 5COPD patient and COPD Foundation community member; 6University of Michigan School of Medicine, Ann Arbor, MI, USA Plain Language Summary What is this summary about? This research gathered insights on the physical, mental, and emotional impact of asthma or chronic obstructive pulmonary disease (COPD) on people. It also looked at how these lung diseases impacted diagnosis, disease management, and health outcomes. What are the key takeaways? In our research, we found that patients often delay seeking medical help until symptoms impact their daily lives including, for example, issues with sleep, exercise, work, relationships, and mental health. A need for better understanding of how to correctly use medications was observed. In this research, patients wanted to know how long it takes to feel improvements after taking a medication. If they have other medical conditions, patients and caregivers want to know about possible side effects. They often used social media or consulted their healthcare providers (HCPs) to improve their understanding and empower themselves in managing their health. What were the main conclusions reported by the researchers? The research concludes that improved access to reliable, trusted information couldimprove patients’ understanding of their disease. This understanding could also improve patients’ communication and partnership with HCPs, helping make informed treatment decisions (shared decision making). What is the purpose of this plain language summary? The purpose of this plain language summary is to help you understand the findings from recent research. The results of this research may differ from those of other studies. HCPs should make treatment decisions based on all available evidence, not on the results of a single study. This is an abstract of the Plain Language Summary of Publication article. To read the full Plain Language Summary of this article, click here to view the PDF. Link to original article here

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)0.011
metaresearch head score (Gemma)0.050
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.050
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0070.008
Open science0.0010.005
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0040.000

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 head0.461
GPT teacher head0.587
Teacher spread0.126 · 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 designQualitative
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

Citations0
Published2025
Admission routes1
Has abstractyes

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