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A positron emission tomography study of the effects of treatment with valproate on brain 5‐HT2A receptors in acute mania

2005· article· en· W1996338031 on OpenAlexafffund
Lakshmi N. Yatham, Peter F. Liddle, Raymond W. Lam, Michael J. Adam, Kevin Solomons, Manjunath Chinnapalli, Thomas J. Ruth

Bibliographic record

VenueBipolar Disorders · 2005
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsTRIUMFUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsStatistical parametric mappingPositron emission tomographyManiaBipolar disorderMoodLithium (medication)Internal medicinePsychologyReceptorMedicineNuclear medicinePsychiatryMagnetic resonance imagingRadiology

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine the effects of treatment with valproate on brain 5-HT2A receptors in acute manic patients using positron emission tomography (PET) and [18F]-setoperone. METHODS: Patients with DSM-IV bipolar I disorder-manic episode were recruited. Patients were drug free or drug naïve at the time of baseline PET scan. All patients were treated with valproate and one patient received lithium in addition to valproate for 3-5 weeks following which they had a post-treatment PET scan. The effect of treatment on brain 5-HT2A receptor binding was determined using statistical parametric mapping (SPM) and region of interest (ROI) analyses. Of the 12 manic patients recruited, seven patients had both baseline and post-treatment PET scans. RESULTS: All seven patients improved with treatment and were in remission at the time of the second PET scan. Both SPM and ROI analyses showed that treatment with mood stabilizers had no significant effect on brain 5-HT2A receptor binding in manic patients. CONCLUSION: This study suggests that changes in brain 5-HT2A receptors are not involved in the antimanic effects of mood stabilizers however, we cannot exclude the possibility of 5-HT2A receptor involvement in down-stream signaling pathways.

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 score0.128
Threshold uncertainty score0.638

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.005
GPT teacher head0.235
Teacher spread0.230 · 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

Citations33
Published2005
Admission routes2
Has abstractyes

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