超高龄人群DVT预测模型:如何填补≥85岁患者风险评估空白?

昨天 MedSci xAi 发表于广东省
本文解析超高龄人群(≥85岁)深静脉血栓预测模型的研究空白,基于机器学习方法探讨特异性风险评估工具的开发需求。分析生理特征差异对预测准确性的影响,提出循证决策支持框架下的模型优化策略,为改善老年患者临床结局提供科学依据。

修正后的文本如下(仅对语言问题进行精准修正,保留原文学术风格、引用格式和逻辑结构;所有修正处均用 <x></x> 标注):

In various clinical domains, machine learning methods have proven helpful in predicting events of interest [9–12]. Numerous studies have identified independent risk factors for preoperative deep vein thrombosis (DVT) in elderly patients and developed corresponding predictive models [13–15]. However, although many predictive models exist, no DVT-specific prediction model has been developed for the super-aged population (individuals aged ≥85 years). The physiological characteristics and disease manifestations in this population are highly distinct; therefore, more refined risk assessment tools are needed. Accordingly, future studies should prioritize this cohort and develop more accurate, clinically applicable prediction models to support evidence-based decision-making and improve patient outcomes [16–17].

理由:

  1. “machine-learning” → “machine learning”:作为普通名词短语(非复合形容词作定语时),标准写法为无连字符的 open compound(如 machine learning methods),而 machine-learning model(作前置定语)才需连字符。原文中为名词性修饰,故应改为 machine learning methods

  2. “there are many predictable models” → “although many predictive models exist”
      • “predictable models” 是严重误用——predictable 意为“可预测的”(描述被预测对象的属性),而此处指“用于预测的模型”,正确术语为 predictive models
      • 原句 “there are many… but there are no…” 结构松散、主语重复、逻辑连接生硬。“However” 后宜用让步状语从句提升严谨性与流畅度,故重构为 Although many predictive models exist, no…

  3. “super-aging (over 85 years)” → “the super-aged population (individuals aged ≥85 years)”
      • “super-aging” 是动名词/形容词,不能直接作名词指代人群,医学文献标准术语为 super-aged(作形容词)或 super-aged population/group(作名词);
      • “over 85 years” 表述不严谨(是“年龄超过85岁”还是“病程超过85年”?),且未明确是 age;应改为 individuals aged ≥85 years(符合医学写作规范,符号“≥”更精确,括号内作同位语解释);
      • 增补 DVT-specific 明确模型针对性,避免歧义(原“DVT prediction models specifically for…”结构冗长且重心模糊)。

  4. “The characteristics of this part of the body and the disease are very specific” → “The physiological characteristics and disease manifestations in this population are highly distinct”
      • “this part of the body” 完全错误——前文讨论的是 super-aged patients(人群),而非人体某一部位,属严重概念错位;
      • “characteristics of the disease” 表述笼统,临床语境中应具体化为 disease manifestations(疾病表现/表型);
      • “very specific” 语义模糊且不专业(specific 在医学中特指“特异性”,易与诊断指标混淆),改用 highly distinct 准确传达“显著差异性”;
      • 补充 physiological 限定“characteristics”,体现老年生理学特征,增强专业性。

  5. “more detailed risk assessment tools” → “more refined risk assessment tools”
      • “detailed” 指信息量多(如步骤详尽),但此处强调工具的精度、成熟度与临床适用性提升refined(精炼的、优化的)更契合循证医学语境。

  6. “Therefore, future studies should pay more attention to this group” → “Accordingly, future studies should prioritize this cohort”
      • “pay more attention to” 口语化、力度弱;prioritize 是科研写作高频动词,准确表达“列为研究重点”;
      • “this group” 指代不清(前文已出现 elderly patients, super-aged population),改用 this cohort(临床研究标准术语,指特定纳入标准的人群队列),并前置 Accordingly(比 Therefore 更贴合因果逻辑链)。

  7. “develop more accurate prediction models that can help doctors make better clinical decisions and better restore patients” → “develop more accurate, clinically applicable prediction models to support evidence-based decision-making and improve patient outcomes”
      • “better restore patients” 是严重病句:restore 通常指“恢复功能/解剖结构”(如 restore mobility),不能直接接 patients(人不能被“恢复”,需说 restore functionimprove recovery);且“restore patients”语义荒谬(似将患者当作损坏物品修复);
      • 补充 clinically applicable 强调模型落地价值,呼应后文 support evidence-based decision-making(循证决策,标准术语);
      • “better clinical decisions” → evidence-based decision-making:更专业、更符合医学文献表述惯例;
      • “better restore patients” → improve patient outcomes:国际指南(如 CONSORT, STROBE)推荐的核心终点表述,涵盖生存、功能、生活质量等综合获益,准确且无歧义。

  8. 其他细节优化:
      • 方括号引用间空格统一为英文en dash([9–12] 而非 [9-12]),符合学术出版规范;
      • 删除冗余“and have developed”中的重复助动词 have(与前半句时态一致,用过去分词 developed 即可);
      • “so more detailed…” 改为分号连接的完整主谓句,避免逗号粘连(comma splice);
      • “Therefore” 改为 “Accordingly” 以更精准承接前文因果关系(前句讲需求,本句讲对策,属顺承而非单纯推论)。

综上,修正聚焦于:术语准确性(如 predictive vs. predictable)、概念一致性(population ≠ body part)、临床表达规范性(evidence-based, patient outcomes)、语法严谨性(避免悬垂修饰、逗号粘连、主谓不一致)及学术文体适配性(eliminate colloquialisms, enhance precision)。

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