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Record W4366815478 · doi:10.4088/pcc.22m03322

Screening for Bipolar I Disorder and the Rapid Mood Screener

2023· article· en· W4366815478 on OpenAlexaff
Michael E. Thase, Stephen M. Stahl, Roger S. McIntyre, Tina Matthews-Hayes, Donna Rolin, Mehul Patel, Amanda Harrington, Vladimir Maletic, Will Jackson, Eduard Vieta

Bibliographic record

VenueThe Primary Care Companion For CNS Disorders · 2023
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMoodBipolar disorderPsychiatryPsychologyClinical psychology

Abstract

fetched live from OpenAlex

Effective screening for bipolar I disorder can lead to enhanced assessment, improved diagnosis, and better patient outcomes. The Rapid Mood Screener (RMS), a new bipolar I disorder screening tool, was evaluated in a nationwide survey of health care providers (HCPs). Eligible HCPs were asked to describe their opinions/current use of screening tools, assess the RMS, and evaluate the RMS versus the Mood Disorder Questionnaire (MDQ). Results were stratified by primary care and psychiatric specialty. Findings were reported using descriptive statistics; statistical significance was reported at the 95% confidence level. < .05); 76% reported that they would screen new patients with depressive symptoms, and 68% indicated they would rescreen patients with a depression diagnosis. Most HCPs (84%) said the RMS would have a positive impact on their practice, with 46% saying they would screen more patients for bipolar disorder. In our survey, the RMS was favorably evaluated by HCPs. A large percentage of respondents preferred the RMS over the MDQ and indicated that it would likely have a positive impact on clinicians' screening behavior.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.917
Threshold uncertainty score0.658

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.021
GPT teacher head0.264
Teacher spread0.243 · 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 designOther design
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

Citations4
Published2023
Admission routes1
Has abstractyes

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