Dying to give birth: the Pakistan Liaison Committee’s strategies to improve maternal health in Pakistan
Bibliographic record
Abstract
Please cite this paper as: Siddiqui G, Hussein R, Dornan J. Dying to give birth: the Pakistan Liaison Committee’s strategies to improve maternal health in Pakistan. BJOG 2011; 118 (Suppl. 2): 96–99. Pakistan has one of the worst maternal mortality ratios worldwide: 260–490 women die for every 100 000 live births in Pakistan. The Pakistan Liaison Group (PLG) was formed to work with and through the international office of the Royal College of Obstetricians and Gynaecologists (RCOG). It works with the RCOG representative committee in Pakistan to improve the health of women. It aims to contribute to improving maternal morbidity and mortality through strategies directed at improving the education and training of health professionals. In addition, the PLG aims to promote changes in the legislature to allow for the notification of maternal deaths so that accurate figures can be obtained, and so that health parameters can be accurately assessed and, in the long term, a confidential enquiry into maternal deaths can be initiated.
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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 | 0.042 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".