Bibliographic record
Abstract
Jacqueline Gardner-Nix1 claims that “It is now acknowledged that opioids may be appropriate in a subset of the population with a variety of conditions that cause chronic pain, including those that are impossible to diagnose exactly.” Chronic nonmalignant pain occurs in a wide range of situations. As rheumatologists, we agree that narcotics are appropriate in some cases, for example, an older patient with serious, painful osteoarthritis of the hip who also has contraindications to surgery. Similarly, where palliative care is the goal, then surely it's appropriate to make the patient's terminal years as comfortable as possible. And for short-term problems, such as post-herpetic neuralgia, narcotics may well allow a patient to enjoy life with adequate function. Conversely, we see a large number of patients — constituting perhaps the largest single diagnostic group in our practice — who have chronic musculoskeletal pain with no clear-cut structural basis. These medically unexplained symptoms include myofascial pain, fibromyalgia and sometimes chronic low back pain. The introduction of narcotics may provide transient pain relief, but no convincing evidence has been published to indicate that they will restore function, get patients back to work or indeed have any long-term benefit whatsoever.2,3 The patients themselves typically describe opioids as merely “taking the edge off the pain.” In treating such patients, the physician must cope not only with underlying pain-avoidance behaviours and fear of a serious structural diagnosis, but also the potential for increasing use of narcotics. In addition, there is the unspoken belief that if narcotics are being used, then the problem must be “really bad,” which may further aggravate the patient's illness behaviour. Therefore, to Box 1 in Gardner-Nix's article,1 which lists barriers to prescribing opioids, we would add the lack of evidence of any long-term beneficial impact, in particular improvement of function or restoration of a more normal lifestyle. In the absence of such evidence, we think a sharp distinction should be drawn between situations where it is appropriate to use narcotics for palliation and situations in which these drugs would not be used under any but extraordinary circumstances. Anthony S. Russell Stephen L. Aaron Department of Medicine Division of Rheumatology/Clinical Immunology University of Alberta Edmonton, Alta.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".