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Record W2040098081 · doi:10.1016/j.jcma.2013.06.013

Taiwan consensus of pharmacological treatment for bipolar disorder

2013· review· en· W2040098081 on OpenAlexaboutno aff
Ya‐Mei Bai, Ching-Jui Chang, Shang‐Ying Tsai, Yi-Chyan Chen, Mei‐Chun Hsiao, Cheng‐Ta Li, Pei‐Chi Tu, Shang‐Wen Chang, Winston W. Shen, Tung‐Ping Su

Bibliographic record

VenueJournal of the Chinese Medical Association · 2013
Typereview
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBipolar disorderConsensus conferenceMEDLINEPsychiatryIntensive care medicineInternal medicineCognition

Abstract

fetched live from OpenAlex

Bipolar disorder is an important psychiatric disorder with different disease phases. The pharmacological treatment is complicated, and is updated frequently as new research evidence emerges. For the purpose of international collaboration, research, and education, the Taiwan consensus of pharmacological treatment for bipolar disorders was initiated by the Taiwanese Society of Biological Psychiatry and Neuropsychopharmacology (TSBPN) - the Bipolar Chapter, which was established in August 2010 and approved as a member of International Society of Bipolar Disorder. TSBPN is the country member of the World Federation of Societies of Biological Psychiatry (WFSBP). The development of the Taiwan consensus for bipolar disorder was mainly based on the template of WFSBP Guidelines, with references to other international guidelines including the Canadian Network for Mood and Anxiety Treatments, and British Association for Psychopharmacology. We have also added Taiwanese experts' experience, Taiwan national health insurance data, and the indications for the pharmacological treatment of bipolar disorder given by the Taiwan Department of Health, to emphasize the balance between efficacy and safety, and to make this consensus a concise, empirical, and important reference for clinical psychiatric practice.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.033
GPT teacher head0.371
Teacher spread0.338 · 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 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

Citations28
Published2013
Admission routes1
Has abstractyes

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