{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T07:42:11Z","timestamp":1791013331587,"version":"4.1.0"},"reference-count":29,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T00:00:00Z","timestamp":1785283200000},"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. Neurol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Functional recovery after Guillain-Barr\u00e9 syndrome (GBS) varies substantially. Prediction models that combine routinely available acute-phase clinical information with traditional Chinese medicine (TCM) syndrome features have not been adequately reported or validated. This study developed an acute-phase dynamic prediction model for 12-month functional outcome in GBS and evaluated discrimination, calibration, and potential clinical utility.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We retrospectively screened 601 patients admitted between January 2015 and December 2023; 525 met eligibility criteria and had 12-month outcome data. The prediction index time was the end of acute in-hospital assessment\/discharge; no post-discharge follow-up variables were used as predictors. Candidate predictors included demographics, acute clinical severity, laboratory and electrophysiological findings, treatment-related acute-care variables, and standardized TCM syndrome elements. Poor outcome was defined as Hughes Functional Grading Scale (HFGS) score &amp;gt;\u202f=\u202f3 at 12\u202fmonths. Patients were stratified into a training cohort (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f368) and a hold-out validation cohort (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f157). Preprocessing, imputation, standardization, feature selection, and synthetic oversampling were performed within the training data only. Logistic regression, random forest, and a fully connected neural-network model were compared. Performance was assessed with 95% confidence intervals, calibration plots, Brier score, decision-curve analysis, and formal AUC comparisons.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Poor 12-month outcome occurred in 148 of 525 patients (28.2%). Mechanical ventilation, admission HFGS score, AMSAN subtype, qi deficiency, and blood stasis were independently associated with poor outcome. In the hold-out validation cohort, AUCs were 0.744 (95% CI 0.655\u20130.833) for logistic regression, 0.843 (95% CI 0.780\u20130.906) for random forest, and 0.887 (95% CI 0.831\u20130.943) for the neural network. The neural network had the lowest Brier score (0.123) and showed acceptable calibration (calibration intercept 0.03; slope 0.94). DeLong testing showed that the neural network outperformed logistic regression (\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.006) but was not statistically superior to random forest (\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.174). Decision-curve analysis suggested net benefit for the machine-learning models across clinically plausible thresholds.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>In this single-center retrospective cohort, acute-phase clinical variables and TCM syndrome elements were associated with 12-month functional outcome in GBS. The neural-network model had the highest numerical AUC and lowest Brier score, but it was not statistically superior to random forest; therefore, no superiority over random forest is claimed. External validation, comparison with established prognostic scores when required variables are available, and prospective implementation studies are required before clinical use.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fneur.2026.1859051","type":"journal-article","created":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T04:36:27Z","timestamp":1785299787000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Predicting 12-month functional outcome in Guillain-Barr\u00e9 syndrome by combining acute-phase clinical data and traditional Chinese medicine syndrome features: a retrospective machine learning study"],"prefix":"10.3389","volume":"17","author":[{"given":"Peng","family":"Hu","sequence":"first","affiliation":[{"name":"Department of Encephalopathy, Changde First Hospital of Traditional Chinese Medicine","place":["Changde, Hunan, 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Medicine","place":["Changde, Hunan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Encephalopathy, Changde First Hospital of Traditional Chinese Medicine","place":["Changde, Hunan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxing","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Encephalopathy, Changde First Hospital of Traditional Chinese Medicine","place":["Changde, Hunan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,29]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"e16365","DOI":"10.1111\/ene.16365","article-title":"Guillain-Barr\u00e9 syndrome: a comprehensive review","volume":"31","author":"Bellanti","year":"2024","journal-title":"Eur J 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