Current and Future Potential Impact of COVID-19 on Kratom (Mitragyna speciosa Korth.) Supply and Use
Bibliographic record
Abstract
Korth., Rubiaceae) is native to and has traditional use in Southeast Asia. The number of kratom users outside of Southeast Asia has increased significantly in recent decades with use spreading to the Unites States (US) and Europe. Because of its reputed opioid-like psychoactive effects at higher doses, kratom has been regulated in several countries and is subject to an import ban by the US Food and Drug Administration. Nonetheless, in the US it is estimated that 10-15 million people consume kratom primarily for the self-treatment of pain, psychiatric disorders, to mitigate withdrawal from or dependence on opioids, and to self-treat opioid use disorder or other substance use disorders (SUDs). Due to the global COVID-19 pandemic, a shortage in the supply of kratom products may place unexpected burdens on kratom users, potentially influencing some who use kratom for SUD self-treatment to regress to harmful drug use, hence increasing the likelihood of adverse outcomes, including overdose. Inadequate treatment, treatment barriers, and increases in the sales of adulterated kratom products on the internet or in convenience stores could exacerbate circumstances further. Although there are currently no verified indications of kratom scarcity, researchers and clinicians should be aware of and remain vigilant to this unanticipated possibility.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".