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Record W2899772523 · doi:10.2147/ndt.s182423

Cognitive functioning in patients treated with electroconvulsive therapy

2018· article· en· W2899772523 on OpenAlexaboutno aff
Lucie Kališová, Markéta Kubinová, J. Michalec, Jakub Albrecht, Katerina Madlova, J Raboch

Bibliographic record

VenueNeuropsychiatric Disease and Treatment · 2018
Typearticle
Languageen
FieldMedicine
TopicElectroconvulsive Therapy Studies
Canadian institutionsnot available
FundersMinisterstvo Zdravotnictví Ceské Republiky
KeywordsMedicineElectroconvulsive therapyCognitionPsychiatryIntensive care medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Electroconvulsive therapy (ECT) is an effective treatment method for severe mental illnesses. ECT has gone through significant modernization. Side effects of ECT have largely decreased. Temporary disturbance of cognitive performance can be still present as a side effect of electroconvulsive treatment. METHODS: Cognitive functioning in the sample of patients with severe and acute mental illness treated with electroconvulsive therapy (ECT) was assessed. Basic assessment of cognitive functions was applied in the beginning, in the middle, and at the end of ECT course treatment with the Montreal Cognitive Assessment (MoCA). Complex and detailed testing of cognitive functions using the MATRICS Consensus Cognitive Battery (MCCB) was done at two points in time - within the first week of and then 6 weeks after the end of ECT. RESULTS: Participants had cognitive deficits at baseline, which were most likely influenced markedly by the psychopathology of the illness itself. The improvement in cognition came together with the reduction in psychopathology; psychopathology scores were significantly reduced during ECT treatment. Compared to the baseline, all scores for cognitive testing were significantly improved but remained low in comparison with the controls. After 6 weeks, there was further significant improvement. CONCLUSION: Our results confirm the safety and efficacy of ECT in the treatment of severe mental disorders.

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)0.000
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 score0.021
Threshold uncertainty score0.680

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 head0.010
GPT teacher head0.248
Teacher spread0.238 · 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

Citations18
Published2018
Admission routes1
Has abstractyes

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