MétaCan
Menu
Back to cohort
Record W2920827634 · doi:10.1017/s1092852919000221

28 How is Postpartum Depression Currently Diagnosed and Managed? Insights from a Virtual Patient Simulation

2019· article· en· W2920827634 on OpenAlexaff
Jovana Lubarda, Martin Warters, Piyali Chatterjee, Marlene P. Freeman, Roger S. McIntyre

Bibliographic record

VenueCNS Spectrums · 2019
Typearticle
Languageen
FieldMedicine
TopicMaternal and fetal healthcare
Canadian institutionsRoyal Victoria Regional Health CentreRoyal Victoria HospitalUniversity Health NetworkUniversity of TorontoCentre for Addiction and Mental HealthRoyal Ottawa Mental Health Centre
FundersSage Therapeutics
KeywordsMedicineMedical diagnosisPostpartum depressionContinuing medical educationPrimary careVirtual patientDepression (economics)Clinical PracticeTest (biology)Family medicineContinuing educationMedical educationPregnancy

Abstract

fetched live from OpenAlex

Abstract Objectives The goal of this study was to determine physician performance in diagnosis and management of postpartum depression (PPD) and to provide needed education in the consequence free environment of a virtual patient simulation (VPS). Methods ∙ A continuing medical education activity was delivered via an online VPS learning platform that offers a lifelike clinical care experience with complete freedom of choice in clinical decision-making and expert personalized feedback to address learner’s practice gaps ∙ Physicians including psychiatrists, primary care physicians (PCPs), and obstetricians/gynecologists (ob/gyns) were presented with two cases of PPD designed to model the experience of actual practice by including use of electronic health records ∙ Following virtual interactions with patients, physicians were asked to make decisions regarding assessments, diagnoses, and pharmacologic therapies. The clinical decisions were analyzed using a sophisticated decision engine, and clinical guidance (CG) based on current evidence-based recommendations was provided in response to learners’ clinical decisions ∙ Impact of the education was measured by comparing participant decisions pre- and post-CG using a 2-tailed, paired t-test; P <.05 was considered statistically significant ∙ The activity launched on Medscape Education on April 26, 2018, and data were collected through to June 17,2018. Results ∙ From pre- to post-CG in the simulation, physicians were more likely to make evidence-based clinical decisions related to: ∙ Ordering appropriate baseline tests including tools/scales to screen for PPD: in case 1, psychiatrists (n=624) improved from 34% to 42% on average (P<.05); PCPs (n=197) improved from 38% to 48% on average (P<.05); and, ob/gyns (n=216) improved from 30% to 38% on average (P<.05) ∙ Diagnosing moderate-to-severe PPD: in case 2, psychiatrists (n=531) improved from 46% to 62% (P<.05); PCPs (n=154) improved from 43% to 55% (P<.05); and, ob/gyns (n=137) improved from 55% to 73% (P<.05) ∙ Ordering appropriate treatments for moderate-to-severe PPD such as selective serotonin-reuptake inhibitors: in case 2, psychiatrists (n=531) improved from 47% CG to 75% (P<.05); PCPs (n=154) improved from 55% to 74% (P<.05); and, ob/gyns (n=137) improved from 51% to 78% (P<.05) ∙ Interestingly, a small percentage of physicians (average of 5%) chose investigational agents for PPD which were in clinical trials pre-CG, and this increased to an average of 9% post-CG Conclusions Physicians who participated in VPS-based education significantly improved their clinical decision-making in PPD, particularly in selection of validated screening tools/scales, diagnosis, and pharmacologic treatments based on severity. Given that VPS immerses physicians in an authentic, practical learning experience matching the scope of clinical practice, this type of intervention can be used to determine clinical practice gaps and translate knowledge into practice. Funding Acknowledgements: The educational activity and outcomes measurement were funded through an independent educational grant from Sage Therapeutics, 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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.445

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)0.0000.000

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 head0.012
GPT teacher head0.257
Teacher spread0.246 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueCNS SpectrumsSame topicMaternal and fetal healthcareFrench-language works237,207