“<scp>Less pain</scp> with <scp>more gain</scp>”—Managing <scp>wound‐related</scp> pain with <scp>cannabis‐based</scp> medicines
Bibliographic record
Abstract
Wound-related pain poses a serious challenge for patients and physicians. It is a complex pathophysiologic construct that may be stratified, from the patient's perspective, into baseline pain and breakthrough pain. The current paradigm for treating wound related pain involves the overuse of opioids and other co-analgesics with little regard for breakthrough pain. These standard medications have a propensity for deleterious side effects while some of them inhibit wound healing, effectively perpetuating the wound and the related pain. In particular, the overuse of opioids is a contributor to the global opioid crisis. It is evident that a new paradigm needs to be considered. Cannabis-based medicines are a prominent prospect under investigation for their potential to reduce dosages of status quo analgesics while effectively reducing pain. The authors propose a new paradigm that emphasizes the use of Cannabis-Based Medicines, delivered through multiple routes, while recommending the need for more foundational scientific investigation into mechanisms, and clinical controlled trials to determine optimal combinations, dosages, and protocols.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".