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Record W2890562542 · doi:10.16995/cg.129

The Story of ECT: Behind the Scenes of a Controversial yet Effective Treatment

2018· article· en· W2890562542 on OpenAlexaff
Annie Zhu, Melissa Phuong, Peter Giacobbe

Bibliographic record

VenueThe Comics Grid Journal of Comics Scholarship · 2018
Typearticle
Languageen
FieldMedicine
TopicElectroconvulsive Therapy Studies
Canadian institutionsSunnybrook Health Science CentreUniversity of OttawaUniversity of Toronto
Fundersnot available
KeywordsElectroconvulsive therapyNarrativeAction (physics)PsychologyNarrative reviewDepictionPsychotherapistClass (philosophy)PerceptionComicsSkepticismMedicinePsychiatryNeuroscienceArtEpistemologyLiteratureCognition

Abstract

fetched live from OpenAlex

<p class="p1">Electroconvulsive therapy (ECT) has been around since the 1930’s, yet it is still associated with passionate controversy. A large contributing factor to the current perception of ECT include the negative and grossly inaccurate portrayals in various forms of media. Through a literature review, the mechanisms, safety, efficacy, and side effects of the therapy are presented in a graphic narrative. Using both words and art to present a more accurate and holistic depiction of ECT, it was decided to use this visual medium so that the information could be accessed by a wide range of readers and counter incorrect depictions. In particular, this narrative could be read by individuals who are interested in learning more about ECT or are considering the treatment, allowing this comic to be a tool to help others make informed decisions. The findings from the review suggest that while the mechanism of action has still yet to be elucidated for ECT, it is an effective treatment in certain severe psychiatric illnesses. Specifically, it can improve symptoms and the quality of life of patients, especially for those who may be resistant to pharmacotherapy.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.420

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.303
Teacher spread0.286 · 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 designBench or experimental
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
Published2018
Admission routes1
Has abstractyes

Explore more

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