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Lamotrigine Treatment for Post-Stroke Pathological Laughing and Crying

2003· article· en· W2033461129 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueClinical Neuropharmacology · 2003
Typearticle
Languageen
FieldHealth Professions
TopicInfant Health and Development
Canadian institutionsFoothills Medical Centre
Fundersnot available
KeywordsLamotrigineCryingStroke (engine)MedicineMoodPathologicalAnesthesiaSerotonin reuptake inhibitorEpilepsyPsychologyPediatricsAntidepressantPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Pathologic laughing and crying (PLC) is a common distressing and socially disabling condition in stroke patients. Antidepressants, particularly selective serotonin reuptake inhibitors (SSRIs), have been increasingly recognized as the treatment of choice for pathologic crying (PC). However, little is known about etiologies and other treatment options for various clinical manifestations of PLC. This case report illustrates the beneficial effect of lamotrigine, a novel antiepileptic drug with antidepressant and mood-stabilizing properties in post-stroke PLC. A 60-year-old woman developed PLC after an ischemic stroke affecting the left frontal and temporal lobes. She was treated with lamotrigine initially at the dose of 50 mg a day, which was gradually increased to 100 mg a day over a 4-week period. There was a significant and rapid recovery in both laughing and crying components of PCL with lamotrigine treatment. The symptoms of pathologic laughing have shown a better response to lamotrigine than PC. Controlled investigations are needed to evaluate the beneficial as well as the differential effects of lamotrigine on PLC.

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.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.894
Threshold uncertainty score0.610

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.217
GPT teacher head0.553
Teacher spread0.337 · 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