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Record W2205861473 · doi:10.1155/2015/456830

Is Valproate Depressogenic in Patients Remitting from Acute Mania? Case Series

2015· article· en· W2205861473 on OpenAlexaff
Kamini Vasudev, Priya Sharma

Bibliographic record

VenueCase Reports in Psychiatry · 2015
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsVictoria HospitalLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineBipolar disorderManiaDosingAntipsychoticAnesthesiaPsychiatryPediatricsInternal medicineLithium (medication)Schizophrenia (object-oriented programming)

Abstract

fetched live from OpenAlex

Valproate is an effective antimanic agent and is recommended as a first-line medication in the treatment of acute mania. Current evidence based guidelines recommend that valproate should be given as a loading dose as it produces a rapid antimanic and antipsychotic response with minimal side-effects. However, no clear guidelines are available on the appropriate dosing or serum levels of valproate in the continuation or maintenance phase of bipolar disorder. We present 4 clinical cases to hypothesize that the higher doses of valproate, such as those used in the treatment of acute mania, may cause a depressive switch. So consideration should be given to reducing the dose of valproate if a patient develops depressive symptoms following recovery from the manic episode, as a therapeutic strategy. The cases also indicate that relatively lower doses and serum levels of valproate are effective in the maintenance phase compared to those needed in the acute manic phase of bipolar disorder. This is the first set of case series that questions the depressogenic potential of valproate in patients remitting from an acute manic episode. It highlights that different doses and serum levels of valproate may be therapeutic in different phases of bipolar disorder.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score0.839

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 head0.019
GPT teacher head0.284
Teacher spread0.265 · 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

Citations4
Published2015
Admission routes1
Has abstractyes

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