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Record W2546410784 · doi:10.1183/13993003.01443-2016

Opioids in COPD: a cause of death or a marker of illness severity?

2016· letter· en· W2546410784 on OpenAlexaff
James Downar, Rebecca E. Colman, Robert Horton, Paul Hernandez, Graeme Rocker

Bibliographic record

VenueEuropean Respiratory Journal · 2016
Typeletter
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsDalhousie UniversityUniversity of Toronto
Fundersnot available
KeywordsCOPDMedicinePulmonary diseaseOpioidIntensive care medicineAdverse effectCause of deathPopulationInternal medicineDisease

Abstract

fetched live from OpenAlex

In a recent issue of the European Respiratory Journal , Vozoris et al. [1] reported a study showing an association between opioid use and increased mortality in patients with chronic obstructive pulmonary disease (COPD). The authors concluded that these results were “worrisome”, and cautioned readers about the “potential for adverse respiratory outcomes” when using opioids in patients with COPD. We are concerned about the authors' interpretation of their data, and the implication that the use of opioids is dangerous in this population. Opioid use associated with mortality in COPD, but dyspnoea-targeted opioids are safe and effective (and good practice)

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)0.003
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.022
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0220.018
Insufficient payload (model declined to judge)0.0040.004

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 head0.042
GPT teacher head0.311
Teacher spread0.269 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations7
Published2016
Admission routes1
Has abstractyes

Explore more

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