Bibliographic record
Abstract
Conflicts of interest: The author is a founding director of a small start up company researching nonpsychotropic cannabinoid formulations for treatment of pain and inflammation Panag Pharma Inc. There is a growing and disturbing trend against the use of medical opioids in the developed world. The fear of addiction continues to contribute to the under treatment of pain and further stigmatization of people living with chronic pain conditions. This fear has recently been fueled by reports of increasing opioid related deaths in a context of what came to be known as nonmedical prescription opioid use (NMPOU) [1]. A number of publications left the impression that the general medical use of opioids was the problem, leading to overly simplistic analyses and interventions that would limit the availability of and access to opioid medications [2,3]. In addition, the media have published articles that are unbalanced, using headlines that exaggerate and polarize the issue and ignore the contribution of other medications [4]. For example, a recent study has identified that the rate of overdose death among those also receiving a benzodiazepine was 10 times higher [5]. This has not been helpful in finding solutions to a complex problem and may have contributed to a fear among physicians of prescribing opioids. In fact, as presented below, the evidence from national surveys supports that since 2011 in the United States and Canada, the general use of opioids has decreased to a significant degree such that those of us on the front lines of pain management are finding that patients are experiencing difficulty accessing opioids when they are medically appropriate. In addition, the negative attention to “pain killers” has further stigmatized and harmed people suffering with pain [6]. The pendulum has swung too far …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".