Patients and Health Care Providers Identify Important Outcomes for Research on Pregnancy and Heart Disease
Bibliographic record
Abstract
Background A core outcome set for studies on cardiac disease in pregnancy is being developed. Incorporating perspectives of patients and health care providers (HCPs) is an essential step in developing this core outcome set, and eliciting these outcomes is the objective of this study. Methods We interviewed pregnant women with heart disease, family members, and HCPs, until data saturation was attained. Participants were asked to share experiences and perspectives, and comment on outcomes they deemed important. Interviews were recorded and transcribed verbatim, and interpretive analysis was used to translate experiences into measurable outcomes. These were classified under 5 core outcome areas, based on a taxonomy of outcomes for medical research. A comparison of the distribution of outcomes within outcome areas, between patients and HCPs, and between interviews and published literature is presented. Results We obtained 17 outcomes from 13 patients and 3 family members, mostly related to general wellness of the baby, congenital anomalies, mental health, and health care delivery; and 45 outcomes from 10 HCPs, which were mostly clinical. Outcomes in published literature when compared with participant interviews put greater emphasis on clinical outcomes (94% vs 76.5%, P = 0.03) and limited emphasis on life impact (0% vs 17.6%, P < 0.001). Conclusions Although clinical outcomes are the main focus of published research in heart disease and pregnancy, patients and HCPs emphasize the importance of outcomes related to general maternal and fetal well-being and life impact, which are seldom reported. Including these outcomes in future studies is essential to facilitating patient-centred care for pregnant women with cardiac disease.
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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 | 0.115 | 0.196 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".