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Record W4409879549 · doi:10.1111/add.70073

The landscape of ketamine use disorder: Patient experiences and perspectives on current treatment options

2025· article· en· W4409879549 on OpenAlexaboutno aff
Rebecca Harding, Tamsin Barton, Maeve Niepceron, Ella Harris, Emily Bennett, E.M. Van Gent, F C Fraser, Celia J. A. Morgan

Bibliographic record

VenueAddiction · 2025
Typearticle
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsnot available
FundersEfficacy and Mechanism Evaluation ProgrammeNational Institute for Health and Care Research
KeywordsSnowball samplingMedicineFeelingMental healthHelp-seekingPsychiatryKetamineClinical psychologyPsychologyFamily medicine

Abstract

fetched live from OpenAlex

AIMS: To report the symptoms and aetiology of ketamine use disorder (KUD), gauge the effectiveness of current treatment services and identify strategies to enhance patient access and outcomes. DESIGN: Mixed-methods, cross-sectional questionnaire. Electronic survey from November 2023 to April 2024. SETTING: Participants were recruited through snowball sampling, social media and referrals from UK addiction treatment services. The survey was open to international participants, with responses collected from the United Kingdom, United States, Canada, Europe and Australia. PARTICIPANTS/CASES: A total of 274 individuals with self-identified KUD, including both treatment-seeking (40%) and non-treatment-seeking (60%) current or former ketamine users. Participants' ages ranged from 18 to 67 years old, with 47.7% identifying as male. Additionally, 58.8% reported a diagnosed mental health disorder. On average, participants consumed 2.0 g of ketamine per day, with treatment-seeking individuals reporting higher average use (M = 2.67 g) than non-treatment-seeking users (M = 1.68 g) (P < 0.001). MEASUREMENTS: Participants completed an online questionnaire addressing their attitudes toward ketamine and treatment services, including questions pertaining to their symptoms of problematic ketamine use, perceptions of education and awareness about KUD, opinions of existing treatment options, and facilitators for seeking treatment. FINDINGS: The study identified various physical symptoms associated with KUD, with bladder problems (60%), nasal problems (60%) and 'K-cramps' (56%) being commonly reported among all users. In response to these symptoms, the majority (56%) did not seek treatment; among treatment-seeking users only 36% reported feeling satisfied with their care. Symptoms of abstinence syndrome were also identified, including cravings (71%), low mood (62%), anxiety (59%) and irritability (45%). Treatment-seeking participants reported that the services they used had little (31%) or some (31%) awareness of ketamine, were not tailored to ketamine use (43%) and were generally only somewhat effective (43%). Fifty-nine percent of participants reported that there was "definitely not" sufficient awareness in education and peer groups about the risks associated with ketamine use. When asked about the most important factors when choosing a treatment program, cost/affordability was the most cited for all participants. CONCLUSIONS: Ketamine use disorder (KUD) appears to be associated with a high prevalence of physical and psychological symptoms, including some specifically linked to abstinence. Despite this, most individuals with KUD do not seek treatment, and existing services are often perceived as ineffective.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.017
GPT teacher head0.283
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2025
Admission routes1
Has abstractyes

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