Cannabis Epistemology and the Case-Series Design: An Invited Commentary on a Case Series and a Dismissive Response
Bibliographic record
Abstract
The publication of ''Cannabidiol in the Treatment of PTSD: A Case Series'' by Elms et al. in the April 2019 JACM proved to be one of the most downloaded of JACM articles in the last two years.Elizabeth Stuyt, MD, issues a strong challenge to the paper, in which well-known integrative psychiatrist Scott Shannon, MD, was a coauthor with Lucas Elms, BS.The rejoinder made clear that there remained a chasm between Stuyt and Shannon regarding the value in publishing a case series with its caveated positive conclusions.To sort through the perspectives, I turned to University of British Columbia cannabinoid research specialist Zach Walsh, PhD, inviting him to review the original paper and the two responses to potentially provide some guidance to the work.Walsh threads a path
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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 | 0.210 | 0.558 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.026 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.012 | 0.008 |
| Research integrity | 0.067 | 0.060 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".