Addictive potential of novel treatments for refractory depression and anxiety
Bibliographic record
Abstract
Dusan Kolar Mood Disorders Research and Treatment Service, Department of Psychiatry, Queen’s University, Kingston, Ontario, Canada Abstract: Treatment-resistant mood disorders and anxiety disorders require intensive treatment, but treatment options should balance benefits and adverse effects or other potential detrimental effects on patients, including the risk of developing prescription medication addiction. Some of the newer treatment modalities for mood and anxiety disorders may have similar properties to benzodiazepines. The goal of this review was to identify the potential for developing dependence on the novel treatment approaches to treatment-resistant depression and refractory anxiety disorders. PubMed, MEDLINE, PsycINFO, Ovid, Cochrane Library, and Google Scholar were searched. Ketamine is effective in improving symptoms of major depressive disorder, but with no sustained benefits. Long-term use of oral or intranasal ketamine formulations may be associated with the risk of developing dependence. Augmentation of stimulant medication is usually effective for residual symptoms of depression, but the effects are usually short lasting and there is a potential for abuse. Synthetic cannabinoids and medicinal cannabis are increasingly being prescribed for a number of medical conditions, including anxiety disorders, without enough evidence about their efficacy and with the risk of patients developing dependence. In summary, benzodiazepines, ketamine, stimulant medications, and cannabinoids have some common characteristics, including short-lasting benefits and the risk of developing prescription medication addiction with longer use. All of these treatments may raise ethical dilemmas about the appropriateness of prescribing these medications in the long run for patients with depression and anxiety disorders. Keywords: treatment-resistant depression, anxiety, addiction, ketamine, cannabinoids
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".