MétaCan
Menu
Back to cohort
Record W4408163199 · doi:10.1097/adm.0000000000001475

A Call for Better Guidance and Treatments for Comorbid Postpartum Depression and Substance Use Disorders

2025· article· en· W4408163199 on OpenAlexafffund
Jeffrey Pan, Kevin Y. Xu, Evan Wood

Bibliographic record

VenueJournal of Addiction Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsBritish Columbia Centre on Substance UseUniversity of British Columbia
FundersCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsMedicinePostpartum depressionPsychiatryDepression (economics)Substance useAntidepressantPostpartum periodComorbiditySerotonergicPregnancyInternal medicineAnxietySerotonin

Abstract

fetched live from OpenAlex

Postpartum depression is a serious, but treatable condition experienced after childbirth. While most cases do not involve excess substance use, alcohol and other substance use have been strongly associated with this condition. While serotonergic antidepressants have been a mainstay of pharmacologic therapy for postpartum depression, studies of antidepressant use in postpartum depression have largely excluded those with substance use disorder, and meta-analyses suggest antidepressants offer limited benefit in those with depression and co-occurring substance use disorder. There is also under-appreciated literature demonstrating the potential for a medication-mediated increase in substance use in some individuals taking serotonergic antidepressants. These facts and an examination of guideline recommendations on the treatment for postpartum depression highlight the need for new research and practice improvements for patients with comorbid substance use disorder and postpartum depression.

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 score0.055
Threshold uncertainty score0.331

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 head0.020
GPT teacher head0.319
Teacher spread0.300 · 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
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Addiction MedicineSame topicMaternal Mental Health During Pregnancy and PostpartumFrench-language works237,207