Prescription opioid use in advanced COPD: benefits, perils and controversies
Bibliographic record
Abstract
In the ancient Roman literary masterpiece Metamorphoses , the poet Ovid writes that the god of sleep, Somnus (who had a twin brother named Thanatos, or Death), lived in a dark cave and “in front of the cave mouth a wealth of poppies flourish” [1]. This verse demonstrates that our ancestors recognised links between sleep, death and the poppy plant (from which opium is derived). Present-day population-based studies show that opioid drugs are used frequently [2, 3] and in other potentially concerning ways [2] among individuals with chronic obstructive pulmonary disease (COPD), including those with nonpalliative disease [2]. Several guidelines [4–6] support using opioids for refractory respiratory symptoms in advanced COPD, which is a commonly encountered and challenging problem. The report by Politis et al. [7] in the European Respiratory Journal describes a case of respiratory depression in an individual with advanced COPD following receipt of prescription opioids for dyspnoea, providing a timely reminder of the serious negative respiratory effects opioids can potentially have in vulnerable COPD patients. Patient and drug regimen selection important with opioid use in advanced COPD, given adverse respiratory event risk
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.020 | 0.025 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".