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Record W2074793989 · doi:10.4212/cjhp.v67i6.1408

Patients’ Physical Response to Thiopental and Alternative Anesthetic Agents in the Setting of Electroconvulsive Therapy

2014· article· en· W2074793989 on OpenAlexafffundvenue
Kayleigh Gordon, Donna M M Woloschuk, Ashley Walus

Bibliographic record

VenueThe Canadian Journal of Hospital Pharmacy · 2014
Typearticle
Languageen
FieldMedicine
TopicElectroconvulsive Therapy Studies
Canadian institutionsWinnipeg Regional Health AuthorityFraser Health
FundersAssociation des pharmaciens du CanadaUniversity of British Columbia
KeywordsElectroconvulsive therapyEconomic shortageAnesthesiaAnestheticMedicineAdverse effectIntensive care medicinePharmacology

Abstract

fetched live from OpenAlex

Drug shortages continue to be at the forefront of health care concerns.1,2Thiopental, an agent used for anesthesia during electroconvulsive therapy (ECT), became unavailable in North America in August 2010. The sole producer of this drug, Hospira (headquartered in Lake Forest, Illinois), ceased production because of liability concerns if thiopental were used via lethal injection to enforce the death penalty.3 The thiopental shortage altered the practice of ECT, forcing a shift to alternative anesthetics. Of concern were the potentially negative consequences of using alternative agents, such as decreased efficacy and increased incidence of adverse effects.2 This study examined ECT session outcomes, relative to recommended criteria for ECT (as described in Appendix 1), to compare patients’ experiences during ECT performed with thiopental (the main anesthetic agent used in 2009) and their experiences during ECT performed with prescriber-selected alternatives used in 2011, when thiopental was in shortage.

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)0.000
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 score0.212
Threshold uncertainty score0.367

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.308
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

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

Citations5
Published2014
Admission routes3
Has abstractyes

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