The drug liking and craving questionnaire (DLCQ) to evaluate addiction risk for ketamine and esketamine
Bibliographic record
Abstract
Ketamine and esketamine (SPRAVATO) are rapid acting antidepressants that have gained evidence and popularity in recent years in treating depression, and are under investigation for other psychiatric indications. Ketamine is a potential drug of abuse and the addiction potential of these medications has been repeatedly cautioned in the psychiatric literature. However, no studies have included a systematic evaluation of addiction potential in their research so risk of this harm remains theoretically and has not been quantified to place in properly in a risk/benefit perspective. There is currently no easily accessible and ready to use tool for this purpose, which poses a barrier to including measures of addiction potential in ketamine and esketamine research. Based on review of the literature and a recently suggested comprehensive Ketamine Side Effect Tool (KSET), as well as the United States Food and Drug Administration recommendations for evaluation addictive potential of a drug, we created a simple, patient rated visual analog scale specifically for assessing ketamine/esketamine drug likeability, cravings and temptation to misuse. While this scale is not validated, in the absence of other accessible tools, use of this questionnaire would provide researchers and clinicians a simple and easy to use tool to begin monitoring addictive potential in patients taking ketamine or esketamine, and better inform future research, clinical practice and publicy policy regarding these substances.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".