An audit of patients currently using legally acquired cannabis as a means of managing chronic pain
Bibliographic record
Abstract
Introduction: Chronic pain constitutes a significant challenge to healthcare today. In Canada, it is estimated that it costs healthcare over $6 billion per year. This audit investigates the medically approved use of cannabis in the treatment of chronic pain in 29 patients at the Alan Edwards Pain Management Unit of the Montreal General Hospital (MGH). \n Methods: Twenty-nine patient charts were accessed at MGH. Relevant patient and usage information were collected from both patient charts and Marijuana Medical Access Regulations (MMAR) documents and compiled into a spreadsheet for analysis. \n Results: Information gathered from 19 males and 10 females revealed that chronic back pain was the most common cause of chronic pain. In addition to cannabis, 11 were currently taking prescribed narcotic medications, five were taking synthetic cannabinoids, and five were using over-the-counter medications. The remaining eight used cannabis alone as their primary means of managing pain. All patients reported improvements in pain after using cannabis. \n Conclusions: This audit suggests that cannabis can be effective at managing mild to moderate levels of pain in patients suffering from a variety of pathologies. Considering the economic, psychological and physical burden associated with pain, and the growing problem of prescribed narcotic dependency around the world, the need for further research into the uses of cannabis as an alternative method of pain management is clear.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".