Patterns of uterine rupture in Nigeria: a comparative study of scarred and unscarred uterus
Bibliographic record
Abstract
Background: Study aims to explore the differences in presentation, management and outcome of uterine rupture among patients with and without previous uterine scar.Methods: A 5-year retrospective cross-sectional study of all cases of uterine rupture in Nigerian Christian Hospital. Data was analyzed using descriptive statistics, chi-square and logistics regression.Results: Incidence of uterine rupture was 14.6 per 1,000 live births (n=70; 40% unscarred uterus, 60% scarred uterus). Lateral rupture was more common with unscarred uterus (n=27; 39%) compared with anterior rupture in scarred uterus (n=43; 71%). Rupture of unscarred uterus often involved the cervix and vagina, while scarred uterus involved the bladder (P= 0.03). More cases with unscarred uterus resulted in hysterectomy (n=3; 14%) versus scarred uterus (n=2; 5%). Unscarred uterus was associated with more haemorrhage 771 (± 670) mL compared with 403 (±585) mL for scarred uterus (P= 0.02). Fetal survival was higher with scarred uterus (P= 0.04). There was no significant difference in the incidence of peri-operative complications between the groups.Conclusion: Rupture of scarred uterus was more common, while rupture of unscarred uterus was associated with more feto-maternal morbidity and mortality. Increase in caesarean section procedures in developing countries could increase the incidence of uterine rupture in these regions, thus prompting a need for improvement in obstetrics care.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".