What to do about Opioids for Chronic Noncancer Pain? A Perspective from Australia
Bibliographic record
Abstract
ISSN 1758-1869 10.2217/PMT.11.59 © 2011 Future Medicine Ltd Pain Manage. (2011) 1(6), 495–497 Many countries in the developed world are awash with opioid overuse, misuse and opioid-related deaths, which have paralleled the increasing medical use of opioids for chronic noncancer pain [1]. This is despite growing concern over the efficacy of these drugs in long-term management. To attempt to reduce the harm associated with opioids but to ensure the continued availability for patient benefit, multiple attempts to develop guidelines, improve regulation and training have recently been attempted or are in progress [2,101,102]. However, unlike other disease areas where the issues are purely medical and hence in principle addressable by guidelines, the issues around appropriate opioid use and misuse go beyond the strictly medical and include the psychological, sociological, legal and regulatory. Hence strategies extending beyond guideline development are likely to be necessary. Given these multiple overlapping areas, it is likely that any solution will not be directly translatable into another healthcare environment although some individual points may be common across jurisdictions. In Australia, our healthcare environment has many similarities with those in western Europe and in Canada being largely based on the public sector with universal insurance and medication reimbursement to some degree. However there are some initiatives and systems unique to Australia, which may put us in a stronger position to find the right balance and methods to achieve Quality Use of Medicines for this difficult class of treatments. Australia is unique in having a National Medicines Policy [103]. This was initially launched in 1999 with the objective to improve positive health outcomes for all Australians through their access to and wise use of medicines. The chair of the eightmember committee, which is responsible for policy development, sits on an executive group which reports directly to the Federal Health Minister. This gives a remarkably short path for an advisory group to access the highest levels of government so that policy recommendations can be actioned. The National Medicines Policy is currently developing a policy on opioid use for chronic noncancer pain. It is reasonable to anticipate that recommendations made will be translated into action. The second unique structural component to our healthcare system is NPS, formerly known as the National Prescribing
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".