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Record W4210695032 · doi:10.1017/s2045796021000731

A critique of narrative reviews of the evidence-base for ECT in depression

2022· article· en· W4210695032 on OpenAlexfundno aff

Bibliographic record

VenueEpidemiology and Psychiatric Sciences · 2022
Typearticle
Languageen
FieldMedicine
TopicElectroconvulsive Therapy Studies
Canadian institutionsnot available
FundersHealth Technology Assessment ProgrammeMedical Research CouncilVictoria General Hospital FoundationH. Lundbeck A/SServierNational Institute for Health and Care ResearchNIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer ResearchNational Institute of Mental HealthLivaNovaWellcome TrustMichael Smith Health Research BCEli Lilly and CompanyNational Alliance for Research on Schizophrenia and DepressionHealth Research BoardCanadian Institutes of Health ResearchSunovionKing's College London
KeywordsNarrativeElectroconvulsive therapyNarrative reviewDepression (economics)Systematic reviewMEDLINE

Abstract

fetched live from OpenAlex

There has been recent debate regarding the efficacy of electroconvulsive therapy in the treatment of depression. This has been based on narrative reviews that contradict existing systematic reviews and meta-analyses. In this special article, we highlight the mistakes that occur when interpreting evidence using narrative reviews, as opposed to conventional systematic reviews and meta-analyses.

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)0.007
metaresearch head score (Gemma)0.004
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 score0.141
Threshold uncertainty score0.512

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.134
GPT teacher head0.443
Teacher spread0.309 · 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

Citations15
Published2022
Admission routes1
Has abstractyes

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