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Record W2942384697 · doi:10.1111/bdi.12781

Lithium and bipolar depression

2019· letter· en· W2942384697 on OpenAlexaff
Mirko Manchia, Janusz Rybakowski, Gabriele Sani, Lars Vedel Kessing, Andréa Murru, Martin Alda, Leonardo Tondo

Bibliographic record

VenueBipolar Disorders · 2019
Typeletter
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsDalhousie University
Fundersnot available
KeywordsScrutinyDepression (economics)Lithium (medication)CitationBipolar disorderPsychologyPsychiatryMedicineClinical psychologyPolitical scienceLawKeynesian economicsEconomics

Abstract

fetched live from OpenAlex

Kelly1 has recently disputed the recommendations of several international guidelines on the use of lithium in bipolar depression. In his scrutiny, the author points to three main errors that seem to have affected systematically ten international guidelines, namely the Woozle effect (evidence by citation), reference inflation (inappropriate citation of pivotal, generally old, studies) and belief perseverance (inability to modify evidence‐based recommendations despite the presence of contrary data). We concur with the author that the evidence supporting the effectiveness of lithium in acute bipolar depression, and to a lesser degree also in major depressive episodes, remains inadequate.2, 3 A different matter is, in our opinion, to label guidelines recommendations as inaccurate or biased, even if, as the author stated, no deceptive intentions were present.

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)0.005
metaresearch head score (Gemma)0.041
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: Editorial · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.041
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0270.020
Insufficient payload (model declined to judge)0.0030.003

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 head0.010
GPT teacher head0.240
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 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
GenreEditorial

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

Citations7
Published2019
Admission routes1
Has abstractyes

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