Innovations in vital signs measurement for the detection of hypertension and shock in pregnancy
Bibliographic record
Abstract
Approximately 820 women die in pregnancy and childbirth every day worldwide, with 99% of these occurring in low-resource settings. The most common causes of maternal mortality are haemorrhage, sepsis and hypertensive disorders. There are established, effective solutions to these complications, however challenges remain in identifying who is at greatest risk and ensuring that interventions are delivered early when they have the greatest potential to benefit. Measuring vital signs is the first step in identifying women at risk. Overstretched or poorly trained staff and inadequate access to accurate, reliable equipment to measure vital signs can potentially result in delayed treatment initiation. Early warning systems may help alert users to identify patients at risk, especially where novel technologies can improve usability by automating calculations and alerting users to abnormalities. This may be of greatest benefit in under-resourced settings where task-sharing is common and early identification of complications can allow for prioritisation of life-saving interventions. This paper highlights the challenges of accurate vital sign measurement in pregnancy and identifies innovations which may improve detection of pregnancy complications.
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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 | 0.006 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 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".