时间标签：数据日期 2026-11-01 · 生成时间 2026-11-01 05:30（Asia/Shanghai）｜ 检索窗口 PubMed edat 2026/10/31–2026/11/01 ｜ 情报发生日期 2026-11-01
研究问题
Introduction Background Osteoporotic vertebral fractures (OVFs) represent one of the most common fragility fractures in older adults and are associated with substantial morbidity, functional decline, and loss of independence.；The affected population is typically characterized by advanced age, a female predominance, and multimorbidity, reflecting the epidemiology of vertebral fragility fractures in aging societies [ , ].
方法
Imputation and model fitting were performed within the training folds; the structural hyperparameters and decision-policy thresholds had been fixed during the preceding model-development phase.；Comparisons across discharge mobility categories (LOW, MID, and HIGH) were performed using Kruskal-Wallis tests for continuous variables and χ 2 tests or Fisher exact tests for categorical variables, as appropriate.；Statistical Analysis Regression analyses were conducted post hoc for clinical characterization and were independent of machine learning model development.
核心结论
Frailty in particular has been linked to increased disability and reduced health-related quality of life in patients with vertebral compression fractures.；In contrast, age, CCI, OF grade, and operative treatment were not significantly associated with discharge mobility (age per 10 y; OR 0.855, 95% CI 0.512-1.430; CCI OR 0.896, 95% CI 0.787-1.021 per point; OF grade OR 0.921, 95% CI 0.649-1.308; treatment OR…；Taken together, the added complexity was not justified by gains in discrimination or calibration, which were modest or absent; its value lay in 3-class differentiation and the reduction of extreme misclassifications.；The higher MID recall relative to the baseline should accordingly be understood as trading overassignment to the middle category for protection against extreme errors, not as improved discrimination of the MID class per se.
局限
Permutation importance was preferred over impurity-based measures, which are biased toward continuous and high-cardinality variables.；Whether this trade-off warrants the additional complexity depends on the clinical weight assigned to avoiding extreme errors.
对本项目的价值
人群纵向证据，可用于假说生成与终点选择：方法学可复用：建模流程与验证策略可直接迁移到衰老时钟 / 终点研究。 ｜ 价值分 1.0/5（★☆☆☆☆）
全文来源
PubMed PMC JATS 全文（正文 35,713 字符，已过 DOI 逐字 + 题名指纹双重归属校验）
原文题录
- 英文标题：Discharge Mobility After Osteoporotic Thoracolumbar Fractures Using a 2-Stage Machine Learning Prediction Model: Prospective Cohort Study.
- 期刊与标识：JMIR formative research · PMID 42955877 · DOI 10.2196/100486
A 级证据分级：A 级（同行评议人体研究）
本文属人体研究证据，但需注意：观察性或小样本干预的结论不等于已证实临床获益；效应量与长期安全性仍需更大规模、更长随访的试验验证。
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