The trend of national and subnational burden of maternal conditions in Iran from 1990 to 2013: the study protocol.
Bibliographic record
Abstract
BACKGROUND: It is widely accepted that maternal mortality is a proxy for maternal health status. Maternal deaths only represent the top of the iceberg; morbidity due to maternal causes apart from maternal mortality, poses a huge burden on women's families. There is an excessive need to widen the research on maternal morbidity. Here, we explain the framework of our study on maternal conditions and their burden in Iran as a part of the National and Sub-national Burden of Diseases (NASBOD) study. METHODS: A systematic search will be carried out for both published and unpublished data on maternal mortality and morbidity reported between 1985 and 2013. Data collected through systematic review and those obtained from national and sub-national surveys will be extracted in a data set. Two statistical models will be applied: Bayesian Autoregressive Multi-level models and Spatio-Temporal Regression models. Models will be used to overcome the problem of data gaps across provinces, years and age groups. DISCUSSION: In order to control and manage maternal conditions and to make more efficient and cost-effective policies, there is an excessive need for data on the burden of such diseases. There are a few sub-national analyses of the burden of disease. In the current study, burden of maternal conditions will be assessed at national and sub-national levels in Iran between 1990 and 2013. The results of this study are undoubtedly required to provide comprehensive information at the national and provincial levels to administer interventions more effectively, since the priority based policies need regional assessments and comparisons.
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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 | 0.026 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 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".