The prevalence of documented cardiovascular-related pregnancy complications: cross-sectional study in an academic primary care centre
Bibliographic record
Abstract
Background Pregnancy and the postpartum period offer a unique opportunity to identify patients with risk factors leading to premature cardiovascular disease (CVD), which often go unrecognised. Aim This study investigates self-reported prevalence of CVD-related pregnancy complications and its documentation in electronic medical records (EMRs) in an academic family health team (AFHT). Design & setting A retrospective cross-sectional survey conducted from 2016 to 2017 in an AFHT. Method The survey assessed self-reported pregnancy complications and obstetric histories of adult females. EMRs of responders who provided consent were appraised for documented pregnancy complications, and management of traditional cardiovascular risk factors post-pregnancy. Results Out of 211 responders, 28% (n= 60) had at least one pregnancy complication reported in the survey and/or in the EMR, of which 67% (n= 40) had the complication documented in their EMR. The most prevalent complications were preterm birth (PTB; 12%,n= 25), hypertensive disorders of pregnancy (HDP; 10%,n= 22), and gestational diabetes mellitus (GDM; 7%,n= 14). Twenty-nine per cent (n= 4) of the patients with GDM had a 75 g oral glucose tolerance test result documented post-pregnancy. Of those with HDP, 36% (n= 8) had body mass index and 50% (n= 11) had a blood pressure measurement recorded after delivery. Conclusion There has been a significant lack of documentation of pregnancy-related cardiovascular risk factors and subsequent management, introducing a missed opportunity for early cardiovascular intervention. Adequate documentation of pregnancy complications in the EMR and better transitions in care between obstetric and primary care teams could potentially enable clinicians to intervene early and better manage females at increased risk of CVD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".