{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T11:10:24Z","timestamp":1788952224893,"version":"build-2803163510"},"reference-count":31,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Surg."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To construct and validate a machine learning-based risk prediction model for poor wound healing after anorectal surgery, enabling early identification and individualized intervention.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Clinical data from 246 patients (Jan 2022\u2013Dec 2024) were retrospectively collected. Wound healing status at 4 weeks post-surgery defined outcome (good:\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009191; poor:\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200955). Independent predictors were identified via multivariate Logistic stepwise regression. Patients were split 7:3 into training and internal validation sets. Three models\u2014Logistic regression, gradient boosting machine (GBM), and random forest (RF)\u2014were developed. Performance was assessed via AUC, calibration curves, Brier scores, and decision curve analysis. Temporal validation was performed using 152 patients from the same center (Jan 2025\u2013Dec 2025) as an independent time-based cohort.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      BMI, diabetes, stool consistency, and preoperative perianal infection were common core predictors. In internal validation, AUCs were 0.893 (Logistic), 0.897 (RF), and 0.877 (GBM). In temporal validation, AUCs were 0.874 (Logistic), 0.865 (GBM), and 0.853 (RF). Logistic regression showed the smallest AUC fluctuation across training, internal, and temporal sets (0.864\u21920.893\u21920.874) and lowest Brier scores (0.092\u20130.123), indicating best calibration and generalizability. GBM achieved the highest training AUC (0.925) but significant performance decay in validation (\n                      <jats:italic>\u0394<\/jats:italic>\n                      AUC\u2009=\u20090.048), suggesting overfitting. After grid search with 10-fold cross-validation, RF attained internal AUC of 0.897 and sensitivity of 0.800, close to Logistic regression, but temporal performance (AUC=0.853, Brier=0.129) was slightly inferior.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Logistic regression demonstrated the best overall discriminative ability, calibration, clinical net benefit, and cross-cohort generalizability, and is recommended as the preferred tool for individualized risk assessment. RF, after thorough optimization, shows promise as an alternative. GBM was limited by sample size and exhibited overfitting, warranting further validation in larger studies. This study provides a quantitative framework for early risk stratification in anorectal surgery patients.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fsurg.2026.1829528","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T10:16:45Z","timestamp":1788949005000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Construction and clinical validation of a machine learning-based risk prediction model for poor wound healing after anorectal surgery"],"prefix":"10.3389","volume":"13","author":[{"given":"Liao","family":"Pan","sequence":"first","affiliation":[{"name":"Proctology Center, The Second Affiliated Hospital of Hunan University of Chinese Medicine","place":["Changsha, Hunan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lizhi","family":"He","sequence":"additional","affiliation":[{"name":"Proctology Center, The Second Affiliated Hospital of Hunan 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