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Depressive relapse during lithium treatment associated with increased serum thyroid‐stimulating hormone: results from two placebo‐controlled bipolar I maintenance studies

2009· article· en· W1977426248 on OpenAlexaff
Mark A. Frye, Lakshmi N. Yatham, Terence A. Ketter, Jack Goldberg, Trisha Suppes, Joseph R. Calabrese, Charles L. Bowden, Eric Bourne, Rebecca S. Bahn, Bryan Adams

Bibliographic record

VenueActa Psychiatrica Scandinavica · 2009
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBipolar disorderLithium (medication)Mood stabilizerLamotriginePlaceboInternal medicineMaintenance therapyMoodThyroid functionPsychologyPost-hoc analysisThyroid-stimulating hormoneMedicineDepression (economics)EndocrinologyThyroidPsychiatryChemotherapyEpilepsy

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess the relationship between depressive relapse and change in thyroid function in an exploratory post hoc analysis from a controlled maintenance evaluation of bipolar I disorder. METHOD: Mean thyroid-stimulating hormone (TSH) and outcome data were pooled from two 18-month, double-blind, placebo-controlled, maintenance studies of lamotrigine and lithium monotherapy. A post hoc analysis of 109 subjects (n = 55 lamotrigine, n = 32 lithium, n = 22 placebo) with serum TSH values at screening and either week 52 (+/-14 days) or study drop-out was conducted. RESULTS: Lithium-treated subjects who required an intervention for a depressive episode had a significantly higher adjusted mean TSH level (4.4 microIU/ml) compared with lithium-treated subjects who did not require intervention for a depressive episode (2.4 microIU/ml). CONCLUSION: Lithium-related changes in thyroid function are clinically relevant and should be carefully monitored in the maintenance phase of bipolar disorder to maximize mood stability and minimize the risk of subsyndromal or syndromal depressive relapse.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.013
GPT teacher head0.276
Teacher spread0.263 · 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.

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

Citations44
Published2009
Admission routes1
Has abstractyes

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