Bibliographic record
Abstract
The Royal College of Obstetricians and Gynaecologists (RCOG), based in London, has a global membership of over 12 000 specialists, nearly 6000 of whom work outside the UK.In recent years, RCOG has felt a major responsibility, together with other partners, including the Liverpool School of Tropical Medicine, to assist our colleagues working in low-and middle-income countries to face up to the challenges outlined in the Millennium Development Goals (MDGs) 4 and 5.We enjoy particular warmth with our global colleagues and those working within Asia.Some years ago, Professor Chatterjee proposed the idea of hosting a South Asia day, to be held here at RCOG in London, which would incorporate experts from Nepal, Bangladesh, Pakistan, Sri Lanka and his own country of India, to share ideas on resolving strategies for MDGs 4 and 5.This supplement represents a synopsis of a remarkable day, and I am very grateful to Nynke van den Broek and Devender Roberts for their initiative in editing this remarkable supplement, which should act as a significant resource to those engaged in this area.These five countries represent one-quarter of the global population.They share similar challenges of difficulty of rural access, poor transport, a limited workforce, limited skills and resources and, sadly, for two of these countries, conflict has been a contemporary problem (Pakistan and Sri Lanka).However, in other ways, they are remarkably different: Sri Lanka being an island in which almost all people enjoy easy access to hospitals, Nepal containing the highest mountains in the world where access to facilities is challenging.Some of these countries have major challenges with education and empowerment of women.Poverty is still a problem for many population groups in these countries, even though there is huge economic development in this region.Sri Lanka, the smallest country of the five, is often applauded as a role model in organising maternity services for other under-resourced countries.With a population of 20 million, the current estimated maternal mortality ratio (MMR) per 100 000 live births in Sri Lanka is 39, a figure that many countries would envy.Professor Senanayake points out that strong political commitment to public health and midwives, together with a realisation of free health care and free education for all, are major
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.548 | 0.509 |
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".