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Record W2762108420 · doi:10.1097/yct.0000000000000461

Predictors of Electroconvulsive Therapy Use in a Large Inpatient Psychiatry Population

2017· article· en· W2762108420 on OpenAlex
Julia A. Knight, Micaela Jantzi, John P. Hirdes, Terry Rabinowitz

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Ect · 2017
Typearticle
Languageen
FieldMedicine
TopicElectroconvulsive Therapy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsElectroconvulsive therapyPsychiatryPopulationMedicineRetrospective cohort studyMoodMood disordersSchizophrenia (object-oriented programming)Internal medicineAnxiety

Abstract

fetched live from OpenAlex

OBJECTIVE: There is limited research on reliable and clinically useful predictors of electroconvulsive therapy (ECT) use. We aimed to examine factors that predict ECT use in an inpatient psychiatric population. DESIGN: Retrospective analysis of provincial database for inpatient psychiatry. METHODS: This study is a retrospective analysis of a provincial database for inpatient psychiatry. The study includes all psychiatric inpatients 18 years or older in Ontario, Canada, assessed with the Resident Assessment Instrument for Mental Health (RAI-MH) within the first 3 days of admission between 2009 and 2014 (n = 153,023). The RAI-MH is a validated assessment tool which includes a breadth of information on symptoms, self-harm, functioning, social support, comorbid medical diagnoses, and risk appraisal. Multivariable analyses were performed using SAS. RESULTS: One hundred forty-five thousand seven hundred (95.2%) of patients admitted had no history of ECT treatment and were not scheduled to receive ECT. A total of 7323 (or 4.8% of the patient population) had either a history of ECT use or were scheduled to receive ECT. Overall rate of ECT use was highest in patients with a provisional diagnosis of mood disorder (7.2%) compared with schizophrenia/other psychotic disorder (3.1%) or substance-related disorder (1.7%). Women were more likely to receive ECT compared with men (overall rates of ECT use 6.2% and 3.4%, respectively). Overall rate of ECT use increased significantly with increasing age. Number of prior hospitalizations was also a strong predictor of ECT use. Conversely, patients with elevated Risk of Harm to Others, schizophrenia, or a substance use disorder were all significantly less likely to receive ECT. All variables examined were statistically significant (P < 0.0001). Higher Severity of Self Harm Scores predicted past use, but not scheduled use of ECT. CONCLUSIONS: This is the largest study to date on predictors of ECT use. Utilization of RAI-MH is a novel and clinically useful method for evaluating predictors of ECT use. Predictors of ECT use within an inpatient population include: presence of a mood disorder, female sex, older age, low risk of harm to others, number of lifetime hospitalizations, lack of substance use disorder, and inability to care for self.

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.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.006
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.311
Teacher spread0.291 · 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