Factors Associated with Caesarean and Peripartum Complications at Southern Mozambique’s Rural Hospitals: A Cross-Sectional Analytical Study
Bibliographic record
Abstract
Information about factors underlying peripartum complications is needed to inform health programs in Mozambique. This retrospective study covered the period from January 2013 to December 2018 and was performed at three rural-district hospitals in southern Mozambique, aiming at assessing factors associated with caesarean and peripartum complications. Data were extracted by clinical criteria-based audits on randomly select clients' files. Logistical regression was used to identify factors associated with peripartum complications. Amongst 5068 audited files, women mean age was 25 years (Standard Deviation (SD) = 7), gestational age was 38 weeks (SD = 2), 25% had "high obstetric-risk" and 19% delivered by caesarean. Factors significantly associated with caesarean included being transferred [Adjusted Odds Ratio (aOR) =1.8; 95% Confidence Interval (95%CI) = 1.3-2.6], preeclampsia [aOR (95%CI) = 2.0 (1.2-3.3)], age [aOR (95%CI) = 0.96 (0.93-0.99)] and "high obstetric-risk" [aOR (95%CI) = 0.54 (0.37-0.78)]. Factors significantly associated with neonatal complication included mother being transferred [aOR (95%CI) = 2.1 (1.8-2.6)], "high obstetric-risk" [aOR (95%CI) = 1.6 (1.3-1.96)], preeclampsia [aOR (95%CI) = 1.5 (1.2-1.8), mother's age [aOR (95%CI) = -2% (-3%, -0.1%)] and gestational age [aOR (95%CI) = -8% (-13%, -6%)] increment. This study identified amendable factors associated with peripartum complications in rural referral health settings. Strengthening hospitals' performance assurance is critical to address the identified factors and improve peripartum outcomes for mothers-neonate dyads.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".