Interconception Care for Primary Care Providers: Consensus Recommendations on Preconception and Postpartum Management of Reproductive-Age Patients With Medical Comorbidities
Bibliographic record
Abstract
Severe maternal morbidity and mortality continue to increase in the United States, largely owing to chronic and newly diagnosed medical comorbidities. Interconception care, or care and management of medical conditions between pregnancies, can improve chronic disease control before, during, and after pregnancy. It is a crucial and time-sensitive intervention that can decrease maternal morbidity and mortality and improve overall health. Despite these potential benefits, interconception care has not been well implemented by the primary care community. Furthermore, there is a lack of guidelines for optimizing preconception chronic disease, risk stratifying postpartum chronic diseases, and recommending general collaborative management principles for reproductive-age patients in the period between pregnancies. As a result, many primary care providers, especially those without obstetric training, are unclear about their specific role in interconception care and may be unsure of effective methods for collaborating with obstetric care providers. In particular, internal medicine physicians, the largest group of primary care physicians, may lack sufficient clinical exposure to medical conditions in the obstetric population during their residency training and may feel uncomfortable in caring for these patients in their subsequent practice. The objective of this article is to review concepts around interconception care, focusing specifically on preconception care for patients with chronic medical conditions (eg, chronic hypertension, chronic diabetes mellitus, chronic kidney disease, venous thromboembolism, and obesity) and postpartum care for those with medically complicated pregnancies (eg, hypertensive disorders of pregnancy, gestational diabetes mellitus, excessive gestational weight gain, peripartum cardiomyopathy, and peripartum mood disorders). We also provide a pragmatic checklist for preconception and postpartum management.
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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 | 0.021 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".