Advances in biomonitoring technologies for women’s health
Bibliographic record
Abstract
In global healthcare systems, sex and gender biases have favored cisgender males, which has led women and transgender individuals to be understudied and underrepresented in medical literature. Thus, these populations are largely overlooked in health policy making. Persistent gender inequalities, socioeconomic divides, and racial-ethnic discrimination, particularly in low-resource communities, have exacerbated women's health concerns, delaying advancements in care and accessibility. However, recent years have seen the emergence of tracking technologies and wearable devices that enable long-term biomonitoring of key health biomarkers which promise to facilitate early disease diagnosis for women from all walks of life. These innovations value education and accessibility, which can break down barriers to health care access and management that has affected generations of women around the world. This review discusses emerging biomonitoring technologies for diagnosing and managing critical women's health conditions as defined by the World Health Organization, including breast and gynecological cancers, vaginal infections, fertility, pregnancy and post-menopausal osteoporosis. Additionally, we examine the current commercial landscape of women's health technologies, highlighting barriers to adoption, such as medical insurance access and socioeconomic status, as well as discuss opportunities for future innovation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".