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Record W2600449400

Using opioids to treat dyspnea in advanced COPD

2012· article· en· W2600449400 on OpenAlexvenueno aff
Joanne Young, Margaret Donahue, Morag Farquhar, Cathy A. Simpson, Graeme Rocker

Bibliographic record

VenueCanadian Family Physician · 2012
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCOPDContext (archaeology)Palliative careQualitative researchFocus groupFamily medicineNursingPsychiatry
DOInot available

Abstract

fetched live from OpenAlex

Objective To explore the experiences of family physicians and respiratory therapists in treating advanced chronic obstructive pulmonary disease (COPD) and their attitudes to the use of opioids for dyspnea in this context. Design Qualitative methodology using one-on-one semistructured interviews. Setting Southern New Brunswick (St Stephen to Sussex). Participants Ten family physicians and 8 respiratory therapists who worked in primary care settings. Methods Participant interviews were audiorecorded, transcribed verbatim, coded conceptually, and thematically analyzed using interpretive description. Main findings Participants reported that patients with advanced COPD often suffered from inadequate control of their dyspnea in advanced stages and that they saw the potential value of opioids in this context; however, family physicians described discomfort prescribing opioids. Barriers included insufficient knowledge, lack of education and guidelines, and fear of censure. Those with palliative care experience tended to be more comfortable with opioid prescribing. Conclusion Findings suggest an important need to address barriers related to more effective treatment of refractory dyspnea in advanced COPD. Further, findings indicate these efforts should focus on effective palliation and innovative educational initiatives, as well as the development, promotion, and uptake of evidence-based practice guidelines related to prescribing opioids for these 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 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.752
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.035
GPT teacher head0.310
Teacher spread0.275 · 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 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

Citations5
Published2012
Admission routes1
Has abstractyes

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