Current approaches for the treatment of ketamine‐induced cystitis
Bibliographic record
Abstract
AIMS: Ketamine is a dissociative anesthetic, historically used in a clinical setting for the induction and maintenance of anesthesia. Ketamine usage can produce undesirable psychological manifestations including hallucinations and long-term psychotomimetic effects. As a results of its fast onset and short period of action, ketamine is widely used as a recreational drug. Chronic abuse of ketamine can lead to significant urinary system complications including ketamine-induced cystitis (KIC). Common side effects of chronic ketamine abuse are urinary pain and discomfort and decreased bladder compliance and voiding pressure. Cessation of ketamine use is associated with improvement of symptoms however the exact pathophysiology of KIC remains unknown, complicating the ability of clinicians to treat this condition. METHOD: A literature search was performed using the National Center for Biotechnology Information (NCBI) Pubmed database up to May 2021. RESULTS: Animal models of KIC are necessary to further our understanding of KIC pathophysiology and explore potential treatment options. In all cases, cessation of ketamine use is the first line of treatment and is most effective in managing KIC. In addition to cessation, treatment plans must be tailored to the individual, based on the severity of symptoms and disease progression, and include options such as: oral anti-inflammatories, intravesical treatment and in the most severe cases, surgical intervention. CONCLUSION: KIC is a painful condition that currently lacks standardized treatment methods. Both animal models of KIC and clinical trials to further elucidate the mechanism of KIC pathophysiology must be explored to create targeted treatment plans.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".