The femtech revolution—A new approach to pregnancy management: Digital transformation of maternity care—The hybrid e‐health perinatal clinic addressing the unmet needs of low‐ and middle‐income countries
Bibliographic record
Abstract
Prenatal care and infant mortality rates are crucial indicators of healthcare quality. However, millions of women in low-income countries lack access to adequate care. Factors such as high-risk pregnancies and unmanaged diet increase the risk of developing complications during pregnancy, highlighting the need for continuous monitoring of maternal health. The increasing burden of non-communicable diseases represents a significant threat to fragile health systems. The lack of access to appropriate prenatal care and poor maternal and newborn health outcomes are major concerns in low- and middle-income countries (LMICs). It emphasizes the need for innovative, integrative approaches to healthcare delivery, especially in pregnant women. The health services need to be reorganized holistically and effectively, focusing on factors that directly impact maternal, neonatal, and infant mortality, resulting in improved access to maternity services and survival of "at-risk" mothers and their offspring in many LMICs. Based on the FIGO (the International Federation of Gynecology & Obstetrics) recommendations of extending preconception care to the postpartum stage, the authors of this review have developed a new model of care-PregCare-based on the triple-intervention-based holistic and multidisciplinary maternal and fetal medicine model for low-risk pregnancies. This model will help transform the traditional model's high visitation frequency into a safe and reduced office visit, while increasing virtual connections, point of care and self-care with doctors, nurses, and community-based providers of self-care. This shall be based on a sophisticated central PregCare call center powered by innovative technologies combined with experienced personnel in perinatal management (doctors and nurses/midwives).
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".