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Record W2032203345 · doi:10.1097/wnf.0b013e3181f8d4ed

Evidence for Use of Mood Stabilizers and Anticonvulsants in the Treatment of Nonaffective Disorders in Children and Adolescents

2010· review· en· W2032203345 on OpenAlexaff
Alfred Amaladoss, Nasreen Roberts, Franklin Amaladoss

Bibliographic record

VenueClinical Neuropharmacology · 2010
Typereview
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsQueen's UniversityHotel Dieu Hospital
Fundersnot available
KeywordsMoodMood disordersLithium (medication)PsychiatryMood stabilizerDivalproexAnticonvulsantPsychologyBipolar disorderLamotrigineClinical psychologyMedicineManiaAnxietyEpilepsy

Abstract

fetched live from OpenAlex

Mood stabilizers and anticonvulsants have been frequently used to control behaviors in children and adolescent with nonaffective disorders. The purpose of this study was to review the literature to evaluate the evidence of these agents as treatment options in this subset of patients. We reviewed all the literature between 1949 and 2009 on the use of anticonvulsants and mood stabilizers in controlling severe behavior dysregulation and aggression in child and adolescent who do not meet the criteria for any mood disorder. The review revealed a total of 19 studies. Of the different mood stabilizers/anticonvulsants, both lithium and divalproex showed some promise in treating children and adolescents with nonmood disorders. Larger studies are nevertheless needed to support the ongoing use of these current anticonvulsants and mood stabilizers in children and adolescents with nonmood disorders. Also, further investigation to the potential use in the long term would need to be established, bearing in mind the balance of side effects and treatment benefit.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.171
GPT teacher head0.475
Teacher spread0.304 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations21
Published2010
Admission routes1
Has abstractyes

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