Frequency of Prenatal Care Visits: Protocol to Develop a Core Outcome Set for Prenatal Care Schedules
Bibliographic record
Abstract
BACKGROUND: Prenatal care, one of the most common preventive care services in the United States, endeavors to improve pregnancy outcomes through evidence-based screenings and interventions. Despite the prevalence of prenatal care and its importance to maternal and infant health, there are several debates about the best methods of prenatal care delivery, including the most appropriate schedule frequency and content of prenatal visits. Current US national guidelines recommend that low-risk individuals receive a standard schedule of 12 to 14 in-office visits, a care delivery model that has remained unchanged for almost a century. OBJECTIVE: In early 2020, to mitigate individuals' exposure to the SARS-CoV-2 virus, prenatal care providers implemented new paradigms that altered the schedule frequency, interval, and modality (eg, telemedicine) of how prenatal care services were offered. In this paper, we describe the development of a core outcome set (COS) that can be used to evaluate the effect of the frequency of prenatal care schedules on maternal and infant outcomes. METHODS: We will systematically review the literature to identify previously reported outcomes important to individuals who receive prenatal care and the people who care for them. Stakeholders with expertise in prenatal care delivery (ie, patients or family members, health care providers, and public health professionals and policy makers) will rate the importance of identified outcomes in a web-based survey using a 3-round Delphi process. A digital consensus meeting will be held for a group of stakeholder representatives to discuss and vote on the outcomes to include in the final COS. RESULTS: The Delphi survey was initiated in July 2022 with invited 71 stakeholders. A digital consensus conference was conducted on October 11, 2022. Data are currently under analysis with plans to submit them in a subsequent manuscript. CONCLUSIONS: More research about the optimal schedule frequency and modality for prenatal care delivery is needed. Standardizing outcomes that are measured and reported in evaluations of the recommended prenatal care schedules will assist evidence synthesis and results reported in systematic reviews and meta-analyses. Overall, this COS will expand the consistency and patient-centeredness of reported outcomes for various prenatal care delivery schedules and modalities, hopefully improving the overall efficacy of recommended care delivery for pregnant people and their families. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/43962.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".