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Record W3159559703 · doi:10.1017/s1092852921000201

Healthcare Provider Perspectives on Bipolar I Disorder Screening and the Rapid Mood Screener (RMS), a Pragmatic, New Tool

2021· article· en· W3159559703 on OpenAlexaff
Michael E. Thase, Stephen M. Stahl, Roger S. McIntyre, Tina Matthews-Hayes, Mehul Patel, Amanda Harrington, Vladimir Maletic, Will Jackson, Eduard Vieta

Bibliographic record

VenueCNS Spectrums · 2021
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsBipolar disorderMoodManiaMedicineMajor depressive disorderObservational studyPsychiatryClinical PracticeDepression (economics)Clinical psychologyFamily medicineInternal medicine

Abstract

fetched live from OpenAlex

Abstract Introduction Although mania is the hallmark symptom of bipolar I disorder (BD-I), most patients initially present for treatment with depressive symptoms. Misdiagnosis of BD-I as major depressive disorder (MDD) is common, potentially resulting in poor outcomes and inappropriate antidepressant monotherapy treatment. Screening patients with depressive symptoms is a practical strategy to help healthcare providers (HCPs) identify when additional assessment for BD-I is warranted. The new 6-item Rapid Mood Screener (RMS) is a pragmatic patient-reported BD-I screening tool that relies on easily understood terminology to screen for manic symptoms and other BD-I features in <2 minutes. The RMS was validated in an observational study in patients with clinically confirmed BD-I (n=67) or MDD (n=72). When 4 or more items were endorsed (“yes”), the sensitivity of the RMS for identifying patients with BP-I was 0.88 and specificity was 0.80; positive and negative predictive values were 0.80 and 0.88, respectively. To more thoroughly understand screening tool use among HCPs, a 10-minute survey was conducted. Methods A nationwide sample of HCPs (N=200) was selected using multiple HCP panels; HCPs were asked to describe their opinions/current use of screening tools, assess the RMS, and evaluate the RMS versus the widely recognized Mood Disorder Questionnaire (MDQ). Results were reported by grouped specialties (primary care physicians, general nurse practitioners [NPs]/physician assistants [PAs], psychiatrists, and psychiatric NPs/PAs). Included HCPs were in practice <30 years, spent at least 75% of their time in clinical practice, saw at least 10 patients with depression per month, and diagnosed MDD or BD in at least 1 patient per month. Findings were reported using descriptive statistics; statistical significance was reported at the 95% confidence interval. Results Among HCPs, 82% used a tool to screen for MDD, while 32% used a tool for BD. Screening tool attributes considered to be of the greatest value included sensitivity (68%), easy to answer questions (66%), specificity (65%), confidence in results (64%), and practicality (62%). Of HCPs familiar with screening tools, 70% thought the RMS was at least somewhat better than other screening tools. Most HCPs were aware of the MDQ (85%), but only 29% reported current use. Most HCPs (81%) preferred the RMS to the MDQ, and the RMS significantly outperformed the MDQ across valued attributes; 76% reported that they were likely to use the RMS to screen new patients with depressive symptoms. A total of 84% said the RMS would have a positive impact on their practice, with 46% saying they would screen more patients for bipolar disorder. Discussion The RMS was viewed positively by HCPs who participated in a brief survey. A large percentage of respondents preferred the RMS over the MDQ and indicated that they would use it in their practice. Collectively, responses indicated that the RMS is likely to have a positive impact on screening behavior. Funding AbbVie Inc.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.871
Threshold uncertainty score0.750

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.262
Teacher spread0.249 · 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 designObservational
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

Citations2
Published2021
Admission routes1
Has abstractyes

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