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Record W2963323531 · doi:10.1186/s40345-019-0151-2

Clinical use of lithium salts: guide for users and prescribers

2019· review· en· W2963323531 on OpenAlexaff
Leonardo Tondo, Martin Alda, Michael Bauer, Veerle Bergink, Paul Grof, Tomáš Hájek, Ute Lewitka, Rasmus Wentzer Licht, Mirko Manchia, B. Müller‐Oerlinghausen, René Ernst Nielsen, Marylou Selo, Christian Simhandl, Ross J. Baldessarini

Bibliographic record

VenueInternational Journal of Bipolar Disorders · 2019
Typereview
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsCentre for Movement DisordersUniversity of TorontoDalhousie University
Fundersnot available
KeywordsLithium (medication)Bipolar disorderDosingMedicineMoodMood disordersTreatment of bipolar disorderIntensive care medicinePsychiatryAdverse effectPsychopharmacologyManiaPharmacologyAnxiety

Abstract

fetched live from OpenAlex

BACKGROUND: Lithium has been used clinically for 70 years, mainly to treat bipolar disorder. Competing treatments and exaggerated impressions about complexity and risks of lithium treatment have led to its declining use in some countries, encouraging this update about its safe clinical use. We conducted a nonsystematic review of recent research reports and developed consensus among international experts on the use of lithium to treat major mood disorders, aiming for a simple but authoritative guide for patients and prescribers. MAIN TEXT: We summarized recommendations concerning safe clinical use of lithium salts to treat major mood disorders, including indications, dosing, clinical monitoring, adverse effects and use in specific circumstances including during pregnancy and for the elderly. CONCLUSIONS: Lithium continues as the standard and most extensively evaluated treatment for bipolar disorder, especially for long-term prophylaxis.

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)0.004
metaresearch head score (Gemma)0.011
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: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0180.017

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 head0.096
GPT teacher head0.421
Teacher spread0.325 · 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

Citations202
Published2019
Admission routes1
Has abstractyes

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