© 2008 Canadian Medical Association or its licensors
Bibliographic record
Abstract
Limiting production of crystal meth I read with interest the recent Public Health piece on methamphetamine hy-drochloride (crystal meth).1 Two subse-quent articles on the same topic pro-vided more details, but there were no comments on prevention programs or on limiting production of this drug.2,3 I had a distinct sense of déjà vu. Forty-five years ago, I reported in CMAJ the first North American case of addiction to diethylpropion.4 This drug is chemically distinct from ampheta-mines, but the symptoms resulting from abuse are identical to those described by Buxton and Dove.1 The only differ-ence is one of degree. My hospital colleagues and I be-lieved that limiting availability was the best way to deal with the abuse prob-lem. We persuaded the manufacturer to have the product made available by prescription, not on demand. This re-duced the problem significantly. I would suggest the same approach be used to address the illegal manufac-ture of crystal meth. It is clear that the manufacturing process is widely known. Is there any chemical used in the production of crystal meth that could be made subject to licensing if it were purchased in large quantities?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.868 | 0.808 |
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".