Global challenges in the identification and management of pre-eclampsia
Bibliographic record
Abstract
Although maternal mortality is thought to be declining, it is estimated that around 800 women die daily as a result of complications of pregnancy. 85% of this burden lies with Sub-Saharan Africa and Southern Asia and all ten of the countries with the highest maternal mortality ratios are in Africa [1]. Pre-eclampsia (defined as hypertension and proteinuria occurring after 20 weeks? gestation of pregnancy) [2] is a leading cause of maternal morbidity and mortality in the developing world, and is also associated with poor fetal outcomes, including fetal growth restriction, and intrauterine fetal death [3]. The World Health Organisation estimates that around 26% of severe maternal pregnancy outcomes are associated with pre-eclampsia [4], and it is thought to be responsible for approximately 40,000 maternal deaths annually [5]. 99% of these deaths occur in developing countries [1]. There are a number of factors and challenges contributing to the stark disparity in pre-eclampsia related morbidity and mortality in the developing world, compared to the developed world. Restricted or under-staffed maternity services, particularly in rural areas with poor transport systems, mean that women in low and middle-income countries may present late or infrequently to antenatal care where pre-eclampsia (a frequently asymptomatic condition) may be diagnosed. However, even when uptake of antenatal healthcare is high, pre-eclampsia remains under-diagnosed. Early and accurate identification of at-risk women depends on regular and accurate blood pressure monitoring [6-8]. However, training in the use of the technically challenging sphygmanometer may be insufficient, and equipment may be lacking or defective [7]. Detection of proteinuria by dipstick has low specificity and depends on a regular supply of accurate urine dipsticks. Once pre-eclampsia is diagnosed (often at a late stage), skilled management is essential (timely delivery of the infant with skilled attendance, preceded by blood pressure control and seizure prevention). However, access to such care may be restricted by transport limitations and referral options, particularly for poor women in rural settings. Once care has been accessed, the quality may be sub-standard. For example, although magnesium sulphate has been shown to be effective for the prevention and control of eclamptic seizures [2] it is yet to be widely used in many low and middle income countries [4]. Efforts to reduce maternal morbidity and mortality due to the complications of preeclampsia in low and middle income countries must be based around improving detection of pre-eclampsia in community and primary care settings, accompanied by improved access to higher-level facilities which can safely induce labour or deliver by caesarean section, with the availability of appropriate high quality medications for blood pressure control and seizure prophylaxis.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".