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Record W6922122504 · doi:10.1192/j.eurpsy.2023.1478

Comparison of prophylactic response to lithium and valproate in patients with Early Onset Bipolar Disorder

2023· article· en· W6922122504 on OpenAlexaboutno aff

Bibliographic record

VenuePubMed Central · 2023
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsLithium (medication)Bipolar disorderNeurocognitiveMoodMood stabilizerValproic AcidObservational studyBipolar illness

Abstract

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INTRODUCTION: The clinical presentation and the course and outcome in Early-Onset Bipolar Disorder (EOBD) patients are found to be atypical compared to adult bipolar patients. Lithium and valproate are among the first-line maintenance treatments for bipolar disorder. Because of atypical features, in many patients, valproate is preferred over lithium. However, recent studies have shown that valproate results in more neurocognitive deficits than lithium. There have been very few Indian studies that assessed the prophylactic response to mood stabilizers in patients with early-onset bipolar disorder. BDNF has an important role in neurodevelopment, and it is shown that peripheral levels of BDNF are reduced in early-onset bipolar disorder. OBJECTIVES: To compare the effectiveness of lithium and valproate in attenuating manic, depressive, and mixed episodes in early-onset bipolar disorder. METHODS: This study was an observational (cross-sectional analytical) study conducted in the Affective Disorder clinic of a tertiary care hospital. We have recruited a total of 50 adult patients with a history of early-onset, i.e., onset at <18 years of age and in remission. Patients were divided into two groups based on the mood stabilizer drug they were receiving. There were 25 patients each in the lithium and valproate group. Montreal Cognitive Assessment (MoCA) scale was applied to assess cognitive functions. RESULTS: The overall functioning was found to be significantly better in the patients receiving lithium than valproate, which was found by higher scores on the Global Assessment of Functioning (GAF) scale.We have found a statistically significant negative correlation between the number of episodes before starting a mood stabilizer and the time to recurrence after starting a mood stabilizer. However, the former cannot predict the latter. The age, educational status of the patient, total duration of illness and number of episodes before starting mood stabilizer correlated significantly with the MoCA score. Of all, the educational status could also predict the patient’s performance on the MoCA scale. CONCLUSIONS: There were no significant differences between lithium and valproate in attenuating further episodes, the frequency of neurocognitive deficits and other adverse effects. Both drugs were equally effective and tolerable. The severity of illness was more in valproate-treated patients, and overall functioning was better in lithium-treated patients. BDNF levels did not correlate with neurocognitive deficits significantly. Future studies comprehensively assessing neurocognitive measures with a larger sample size in the early-onset bipolar disorder population would shed more light on the role of biomarkers in cognition in subjects with early-onset bipolar disorder. DISCLOSURE OF INTEREST: None Declared

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.014
GPT teacher head0.255
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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