Timing of maternal and neonatal mortality and morbidity in healthy women and newborns during the postnatal period: a systematic review protocol
Bibliographic record
Abstract
OBJECTIVE: To determine the timing of overall and cause-specific maternal and neonatal mortality and severe morbidity in healthy women and newborns during the postnatal period. INTRODUCTION: Despite significant focus on improving maternal and neonatal outcomes, many women and newborns continue to die or suffer negative health outcomes within the postnatal period. While the maternal and neonatal mortality and morbidity rates and causes are regularly updated, less is known on when they occur during the postnatal period. INCLUSION CRITERIA: This review will consider studies that include healthy women and newborns after birth to six weeks' and four weeks' postnatally, respectively, and includes data regarding time to death or complications. Studies that report solely on high risk women (eg, antenatal complications) or preterm or high-risk newborns will not be included in this review. METHODS: The search strategy will aim to locate both published and unpublished studies. After the initial search and removal of duplicates, titles and abstracts of all retrieved studies will be screened and the full text of selected reports will be assessed against the eligibility criteria. The reference list of all studies selected for critical appraisal will be screened for additional relevant studies/reports. Screening, critical appraisal, and data extraction will be completed by two independent reviewers. Findings from the studies/reports will be pooled in statistical meta-analysis or presented in narrative form including tables and figures. SYSTEMATIC REVIEW REGISTRATION NUMBER: PROSPERO CRD42020187341.
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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 | 0.068 | 0.069 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.021 | 0.018 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.055 | 0.008 |
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".