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Record W3170794346 · doi:10.1177/1753495x211012502

Prenatal electrocardiogram testing and postpartum depression: A population-based cohort study

2021· article· en· W3170794346 on OpenAlexaffabout
Jonathan S. Zipursky, Deva Thiruchelvam, Donald A. Redelmeier

Bibliographic record

VenueObstetric Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsHealth Sciences CentreInstitute for Clinical Evaluative SciencesSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineDepression (economics)Odds ratioPregnancyPostpartum depressionObstetricsCohortPopulationPrenatal carePostpartum periodCohort studyMoodConfidence intervalEdinburgh Postnatal Depression ScalePsychiatryPediatricsInternal medicineAnxiety

Abstract

fetched live from OpenAlex

Background Cardiovascular symptoms in pregnancy may be a clue to psychological distress. We examined whether electrocardiogram testing in pregnant women is associated with an increased risk of subsequent postpartum depression. Methods We conducted a population-based cohort study of pregnant women who delivered in Ontario, Canada comparing women who received a prenatal ECG to women who did not. Results In total, 3,238,218 women gave birth during the 25-year study period of whom 157,352 (5%) received an electrocardiogram during prenatal care. Receiving an electrocardiogram test was associated with a one-third relative increase in the odds of postpartum depression (odds ratio 1.34; 95% confidence interval 1.29–1.39, p < 0.001). Conclusion The association between prenatal electrocardiogram testing and postpartum depression suggests a possible link of organic disease with mental illness, and emphasizes that cardiovascular symptoms may be a clinical clue to the presence of an underlying mood disorder.

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)0.002
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 score0.011
Threshold uncertainty score0.669

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.022
GPT teacher head0.297
Teacher spread0.275 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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