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Record W94993519 · doi:10.1139/jpn.0554

Long-term lithium treatment and thyroid antibodies: a controlled study

2005· article· en· W94993519 on OpenAlexvenueno aff
Christopher Baethge, Holger Blumentritt, Anne Berghöfer, Tom Bschor, Tasha Glenn, Mazda Adli, Peter Schlattmann, Michael Bauer, R. Finke

Bibliographic record

VenueJournal of Psychiatry and Neuroscience · 2005
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsTerm (time)Lithium (medication)AntibodyThyroidMedicineInternal medicineImmunologyPhysics

Abstract

fetched live from OpenAlex

OBJECTIVE: Because the role of thyroid autoimmunity in the development of lithium-induced thyroid dysfunction remains controversial, we compared the prevalence of thyroid autoantibodies in patients with affective disorders receiving long-term lithium maintenance therapy with that of age- and sex-matched controls. METHODS: We conducted a cross-sectional study with 100 adult patients with major affective disorders diagnosed according to the Diagnostic and Statistical Manual of Mental Disorders, revised (DSM-III-R), who were undergoing lithium therapy for 6 months or more at a specialized lithium university clinic and 100 age- and sex-matched controls with no history of an axis I psychiatric disorder. Serum autoantibodies against thyroid peroxidase (TPOAb), thyroglobulin (TgAb) and TSH receptors (TRAb) were measured. RESULTS: TPOAb were found in 7 patients and 11 controls, and TgAb were found in 8 patients and 15 controls. TRAb were not found in either group. CONCLUSIONS: In this sample of patients with affective disorders, long-term lithium treatment did not increase the prevalence of thyroid autoimmunity.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.018
GPT teacher head0.307
Teacher spread0.289 · 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 designNon-randomized trial
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

Citations53
Published2005
Admission routes1
Has abstractyes

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