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Record W7132929677

Real-world Effectiveness of Intravenous Ketamine for Suicidal Ideation in Treatment-Resistant Depression

2024· dissertation· W7132929677 on OpenAlexaboutno aff
David Chen-Jun Chen-Li

Bibliographic record

VenueTSpace · 2024
Typedissertation
Language
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsnot available
Fundersnot available
KeywordsSuicidal ideationKetamineDepression (economics)MoodAntidepressantClinical trialKetamine hydrochlorideDepressive mood
DOInot available

Abstract

fetched live from OpenAlex

Suicidal ideation is a significant symptom associated with mood disorders. Clinical trials havedemonstrated rapid reduction in suicidal ideation following ketamine infusions, however, clinical trial results are not always generalizable to real world practice. A real world effectiveness analysis of 96 adult outpatients with treatment-resistant depression (TRD) that received ketamine infusions in a community clinic in Toronto, Ontario was conducted. Suicidality and depressive symptom outcomes were assessed with the Columbia Suicide Severity Rating Scale (C-SSRS) (self-report version) and the Quick Inventory for Depressive Symptomatology Self-Report 16-Item respectively. Mean C-SSRS score significantly decreased following a single ketamine infusion and was indicative of a reduction in suicidality from active to passive suicidal ideation on a group level. Results of the mediation analysis indicated that the antisuicidal effects of ketamine are partially independent of its antidepressant effects. This study suggests that ketamine is effective in reducing suicidal ideation in a real-world setting with benefits comparable to clinical trials.

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)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.021
GPT teacher head0.376
Teacher spread0.355 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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