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A three-fold cross-validation technique was applied with subsets for data training and testing in Python\u00ae (version 3.8) using the most predictive factors. The model performance was then compared to the previously published predictive algorithms.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The retrospective model showed good predictive accuracy with an AUC of 0.76 (95% CI: 0.71\u20130.83) for spontaneous preterm birth, with a sensitivity and specificity of 0.71 (95% CI: 0.66\u20130.76) and 0.78 (95% CI: 0.63\u20130.88) respectively based on seven variables: maternal age, BMI, ethnicity, smoking, gestational type, substance misuse and parity\/obstetric history.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Pending further validation, our observations suggest that key maternal demographic features, incorporated into a traditional mathematical model, have promising predictive utility for spontaneous preterm birth in pregnant women in our region without the need for cervical length and\/or fetal fibronectin.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12884-024-06892-3","type":"journal-article","created":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T13:02:57Z","timestamp":1729515777000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Developing a logistic regression model to predict spontaneous preterm birth from maternal socio-demographic and obstetric history at initial pregnancy registration"],"prefix":"10.1186","volume":"24","author":[{"given":"Brenda F.","family":"Narice","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mariam","family":"Labib","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengxiao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Victoria","family":"Byrne","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joanna","family":"Shepherd","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Z. 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