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Record W4375955590 · doi:10.1097/yic.0000000000000462

Ketamine for depression: a potential role in requests for Medical Aid in Dying?

2023· article· en· W4375955590 on OpenAlexaffabout
Nicolas Garel, Michka Nazon, Kamran Naghi, Elena Willis, Karl Looper, Soham Rej, Kyle T. Greenway

Bibliographic record

VenueInternational Clinical Psychopharmacology · 2023
Typearticle
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsJewish General HospitalUniversité de MontréalMcGill University
Fundersnot available
KeywordsDepression (economics)PsychiatryKetamineIntervention (counseling)MoodMedicineMajor depressive disorderPsychotherapistPsychology

Abstract

fetched live from OpenAlex

Medical Aid in Dying (MAiD) is the act of a healthcare provider ending a patient's life, at their request, due to unbearable suffering from a grievous and incurable disease. Access to MAiD has expanded in the last decade and, more recently, it has been made available for psychiatric illnesses in a few countries. Recent studies have found that such psychiatric requests are rapidly increasing and primarily involve mood disorders as the primary condition. Nevertheless, MAiD for psychiatric disorders is associated with significant controversy and debate, especially regarding the definition and determination of irremediability - that a given patient lacks any reasonable prospect for recovery. In this article, we report the case of a Canadian patient who was actively requesting Medical Assistance in Dying for severe and prolonged treatment-resistant depression until she experienced remarkable benefits from a course of intravenous ketamine infusions. To our knowledge, this is the first report of ketamine or any other intervention yielding remission in a patient who would have otherwise likely been eligible for MAiD for depression. We discuss implications for the evaluation of similar requests and, more specifically, why a trial of ketamine warrants consideration.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score0.682

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.048
GPT teacher head0.495
Teacher spread0.447 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations7
Published2023
Admission routes2
Has abstractyes

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