Bibliographic record
Abstract
Authors’ reply Zainab Ahmadi, 1 David C Currow, 2 Magnus Ekström 1,2 1 Department of Clinical Sciences, Division of Respiratory Medicine and Allergology, Lund University Hospital, Lund, Sweden; 2 Discipline, Palliative and Supportive Services, Flinders University, Adelaide, SA, Australia We thank Dr Vozoris for his insightful comments on our paper. 1 The use of opioids for treating pain and the underlying evidence base for this indication was not the scope of our article. Although we agree that the evidence for treatment with opioids for “chronic” musculoskeletal pain is inconsistent or weak, we had insufficient data to determine symptom severity and whether the patients were prescribed opioids for chronic or acute pain. It should also be considered that the cited Cochrane reviews on opioids for chronic pain have weak evidence for their conclusions. 2,3 The review of long-term effectiveness and safety of opioid therapy for chronic noncancer pain by Noble et al 2 included 25 case series and only 1 randomized controlled trial. The clinician should carefully weigh the risk versus benefit of opioids in pain treatment, especially in the setting of clinical instability and in chronic pain. However, we think that there are many situations where opioids have an important role in treating severe distressing pain, where failure to use opioids might contribute to unnecessary suffering and treatment nihilism. Read the original article by Ahmadi et al
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 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 | 0.006 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.040 | 0.036 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".