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Record W2995316431 · doi:10.21037/apm.2019.11.04

Opioid utility for dyspnea in chronic obstructive pulmonary disease: a complicated and controversial story

2020· review· en· W2995316431 on OpenAlexaff
Nicholas T. Vozoris

Bibliographic record

VenueAnnals of Palliative Medicine · 2020
Typereview
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsSt. Michael's HospitalInstitute for Clinical Evaluative SciencesUniversity of Toronto
Fundersnot available
KeywordsMedicineCOPDIntensive care medicineOpioidObservational studyRandomized controlled trialOpioid use disorderPopulationPharmacotherapyInternal medicine

Abstract

fetched live from OpenAlex

Chronic obstructive pulmonary disease (COPD) is common chronic respiratory disorder, predominantly caused by exposure to cigarette smoke or biomass fuels, and it usually affects older adults. Dyspnea in COPD that is unresponsive to traditional management is a challenging disease complication for both the patient and the health care professional. Off-label use of opioids has been advocated as a pharmacotherapy strategy for refractory dyspnea. However, negative respiratory outcomes are a potential concern with opioids drugs, especially among individuals with COPD. In this review, randomized controlled trials evaluating opioid efficacy among individuals with COPD are reviewed and critically analyzed, and data from observational drug safety studies is also presented. In summary, the evidence in support of using opioids for refractory dyspnea in COPD is minimal and weak, and there is mounting data demonstrating that opioids are associated with increased respiratory-related morbidity and mortality in this population. Therefore, current evidence does not support the broad application of opioids for refractory dyspnea among individuals with COPD. However, there may be subsets of individuals that experience modest improvement in dyspnea with opioids, and better understanding predictors and mechanisms of such opioid responsiveness should be a focus of future research endeavours.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.875
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.130
GPT teacher head0.420
Teacher spread0.290 · 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 designOther design
Domainnot available
GenreReview

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

Citations12
Published2020
Admission routes1
Has abstractyes

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