Towards decentralized diagnostics for women's health using silicon photonic integrated circuits
Bibliographic record
Abstract
Despite historical neglect, quantitative hormone measurement is crucial for understanding the menstrual cycle (the “fi fth vital sign”) and conditions in women's health such as endometriosis, fertility, pregnancy loss, and menopause. Because hormone levels fl uctuate and interact dynamically during the menstrual cycle, single or infrequent measurements are insuffi cient and several hormones need to be measured simultaneously. There remains a critical need for longitudinal, high-sensitivity, multi-hormone monitoring to enable meaningful insights and advance women's health, both in research and in the clinic. Current hormone measurement technologies are unable to meet this need and suff er from tradeoff s between quantitative accuracy, multiplexing for simultaneous measurement of multiple markers, and cost/convenience. Lab-based measurements are too expensive and inconvenient for frequent (ideally daily) use, while existing portable technologies like lateral fl ow assays are limited to detecting 3-4 hormone markers. New technologies to quantitatively and conveniently monitor hormone levels would have the potential to catalyze a new era of women's health research as well as give clinicians critical evidence to inform how we diagnose and treat conditions in women's health, revolutionizing the standard of care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".