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Patterns of uterine rupture in Nigeria: a comparative study of scarred and unscarred uterus

2015· article· en· W1852004062 on OpenAlexaff
Kelechi Eguzo, Adegboyega Lawal, Farzana Ali, Chisara C. Umezurike

Bibliographic record

VenueInternational Journal of Reproduction Contraception Obstetrics and Gynecology · 2015
Typearticle
Languageen
FieldMedicine
TopicMaternal and Perinatal Health Interventions
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsUterine ruptureUterusMedicineIncidence (geometry)ObstetricsGynecologyHysterectomySurgeryInternal medicine

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.055
GPT teacher head0.374
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2015
Admission routes1
Has abstractyes

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