Body mass index‐related cesarean section complications in sub‐Saharan Africa: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Obesity and cesarean section (CS) rates are rising in sub-Saharan Africa (SSA), where risks for complications that adversely affect maternal health, such as infections, are high. OBJECTIVE: To conduct a systematic review and meta-analysis to report on the incidence and types of body mass index (BMI, calculated as weight in kilograms divided by the square of height in meters)-related complications following CS in SSA. SEARCH STRATEGY: A systematic search was conducted in PubMed/MEDLINE, EMBASE, and Global Health Library up to August 2020 using (MeSH) terms related to CS, BMI, and SSA. SELECTION CRITERIA: Quantitative studies that evaluated BMI-related complications of CS in English. DATA COLLECTION AND ANALYSIS: Data were extracted using a standardized form. The risk of bias was assessed using the Newcastle-Ottawa Scale. The incidence of BMI-related complications at 95% confidence interval was calculated and a meta-analysis conducted. MAIN RESULTS: Of 84 articles screened, five were included. Complications associated with a higher BMI were: wound infection, hemorrhage, post-dural puncture headache, and prolonged surgery time in comparison with patients with a normal BMI. Women with a high BMI (>25.0) have a two-fold increased risk for post-cesarean wound infection compared with women with a normal BMI (20.0-24.9) (odds ratio 1.91, 95% confidence interval 1.11-3.52). CONCLUSION: Overweight and obesity were associated with CS complications in SSA, but limited research is available.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.027 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".