Investigation of stillbirths in Brazil: A systematic scoping review of the causes and related reporting processes in the past decade
Bibliographic record
Abstract
BACKGROUND: Recognizing the causes of stillbirths and their associated conditions is essential to reduce its occurrence. OBJECTIVE: To describe information on stillbirths in Brazil during the past decade. SEARCH STRATEGY: A literature search was performed from January 2010 to December 2020. SELECTION CRITERIA: Original observational studies and clinical trials. DATA COLLECTION AND ANALYSIS: Data were manually extracted to a spreadsheet and descriptive analysis was performed. RESULTS: A total of 55 studies were included; 40 studies (72.2%) used the official data stored by national public health systems. Most articles aimed to estimate the rate and trends of stillbirth (60%) or their causes (55.4%). Among the 16 articles addressing the causes of death, 10 (62.5%) used the International Classification of Diseases; most of the articles only specified the main cause of death. Intrauterine hypoxia was the main cause reported (ranging from 14.3% to 54.9%). CONCLUSION: Having a national system based on compulsory notification of stillbirths may not be sufficient to provide quality information on occurrence and, especially, causes of death. Further improvements of the attribution and registration of causes of deaths and the implementation of educational actions for improving reporting systems are advisable. Finally, expanding the investigation of contributing factors associated with stillbirths would create an opportunity for further development of prevention strategies in low- and middle-income countries such as Brazil.
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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 | 0.047 | 0.158 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.031 | 0.031 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".