把中文文本翻译成英文，并选择合适位置有效融合至上下文中，用学术论文的语气（不允许出现任何不专业的：我...
Creato il: 9 novembre 2025
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下面给出可直接替换到手稿中的英文段落，已将中文内容精准翻译并与上下文无缝融合；措辞为正式学术体，避免了第一人称与主观表述。出处均来自上传的文稿。(manuscript.docx)
Methods → Exposure（替换原句含“入院”的那一行）
Revised sentence
The index time was defined as ICU admission, and the primary exposure was the BAR calculated from the earliest available BUN and albumin values within the first 24 hours of the ICU stay, with units harmonized a priori before computing BAR. (manuscript.docx)
Results（在“Table 1”之后、“Figure 2”之前插入以下小节）
3.3 Kaplan–Meier Survival Curves
Kaplan–Meier curves stratified by BAR tertiles (T1–T3) exhibited clear, stable, and progressively greater separation by 30 days (log‑rank P < 0.001; Fig. 2A), with persistence of the separation at 60, 120, and 365 days (Fig. 2B–D). These visual patterns indicate that higher BAR at ICU admission is associated with poorer short‑ and long‑term survival without attenuation of the risk gradient over extended horizons, suggesting temporally durable prognostic information carried by BAR. (manuscript.docx)
3.4 Multivariable Cox Proportional‑Hazards Models
After full adjustment (Model 3), each 1 mg·g⁻¹ increase in BAR was associated with approximately a 2.6%–2.7% higher hazard of death at 30, 60, 120, and 365 days (30 days: HR 1.026, 95% CI 1.011–1.041; 60 days: HR 1.027, 95% CI 1.013–1.041; 120 days: HR 1.027, 95% CI 1.013–1.041; 365 days: HR 1.026, 95% CI 1.013–1.040; all statistically significant). In tertile analyses, adjusted HRs for T3 vs T1 consistently lay around 1.75–1.81 at each time point (30 days: 1.803; 60 days: 1.752; 120 days: 1.813; 365 days: 1.812; all P = 0.001), whereas T2 vs T1 did not reach statistical significance in Model 3. Tests for linear trend were significant at all time points (30 days Ptrend = 0.001; 60 days = 0.001; 120 days < 0.001; 365 days < 0.001). Overall, estimates were directionally consistent and stable in magnitude, indicating risk concentration at higher BAR levels and aligning with the Kaplan–Meier separation. The proportional‑hazards assumption was not violated, supporting model appropriateness (Table 2). (manuscript.docx)
3.4.1 Restricted Cubic Splines (Dose–Response)
Restricted cubic spline analyses at 30 days demonstrated a positive, approximately linear dose–response between BAR and mortality, with confidence intervals predominantly above HR = 1 and no clear threshold effect (Fig. 3A). The same pattern was reproduced at 60, 120, and 365 days (Fig. 3B–D), indicating that risk accumulation with increasing BAR is not confined to extremes but rises smoothly across clinically common ranges. These findings corroborate the linear‑trend tests from Cox models and the stratified separation on Kaplan–Meier curves, underscoring the interpretability and robustness of BAR as a continuous predictor. (manuscript.docx)
3.4.2 Subgroup and Interaction Analyses
Across prespecified clinical subgroups—spanning demographics, comorbidities, severity strata, and intervention status (IMV/CRRT)—the direction and magnitude of BAR‑associated risk at 30 days were consistent, with no significant interactions (all P‑interaction > 0.05; Fig. 4). Extending the window to 60, 120, and 365 days yielded concordant results without evidence of effect modification (Supplementary Figs 1–3). Proportional‑hazards tests were not violated for the overall model or individual covariates (all P > 0.05). (manuscript.docx)
3.5 Machine‑Learning Validation（整体替换本小节的正文描述）
3.5.1 Discrimination: Concordant Evidence from ROC and PR Curves
Using 30‑day mortality as the primary endpoint, eight supervised algorithms—including gradient‑boosted trees (XGBoost), random forests, and regularized logistic regression—were trained and evaluated by cross‑validated grid search. Models showed acceptable‑to‑good discrimination on ROC‑AUC and precision–recall curves (average precision), with XGBoost performing best and leading consistently across plots (main Fig. 5; Supplementary Fig. S4). Given the event rate (~37.3%), ROC‑AUC supported overall comparison, whereas PR curves better reflected the ability to capture true positives in clinically prioritized high‑risk regions; both metrics led to consistent conclusions, indicating enhanced case capture when BAR was included among predictors. (manuscript.docx)
3.5.2 Clinical Utility: Threshold‑Based Net Benefit on DCA
Decision‑curve analysis demonstrated sustained net benefit across a range of clinically reasonable threshold probabilities compared with “treat‑all” or “treat‑none” strategies. Ensemble models, exemplified by XGBoost, yielded superior decision value for triage or escalation decisions within threshold‑driven workflows (Supplementary Fig. S5). These findings imply that models incorporating BAR can reduce unnecessary interventions while retaining a higher proportion of patients who truly require intervention. (manuscript.docx)
3.5.3 Interpretability: SHAP Shows a Stable, Monotonic Contribution of BAR
Global importance rankings and beeswarm plots indicated that BAR consistently ranked among the most influential predictors across models, with higher values associated monotonically with increased predicted mortality. At the individual level, force plots showed that higher BAR shifted the prediction toward death through cumulative contributions (main Figs. 6–7; Supplementary Fig. S7). These interpretability results align with multivariable Cox and spline findings, indicating that the BAR signal is clinically meaningful rather than a black‑box artifact. (manuscript.docx)
3.5.4 Feature Stability: Boruta Corroborates the Non‑Redundancy of BAR
Feature‑selection stability assessed by Boruta—via comparison with shadow features—showed prominent and high‑ranked importance for BAR, overlapping with SHAP‑identified influential predictors (Supplementary Fig. S6). Together with discrimination and decision‑curve evidence, these results support the consistent advantage of BAR across modeling frameworks and evaluation dimensions. (manuscript.docx)
Discussion（将下列两段分别嵌入到现有讨论段落中）
放在讨论开头第二段之后，承接“subgroups showed no evidence of effect modification.”
The non‑significant difference between the middle tertile (T2) and T1 is more compatible with insufficient evidence rather than absence of effect and accords with risk concentration at higher BAR levels. These conclusions are consistent with subgroup analyses, which showed no significant interactions (all P‑interaction > 0.05). (manuscript.docx)
放在关于病理生理学与临床评分体系的段落之前，起到文献综述与转场作用（对应参考文献 11–15）
Cardiogenic shock is a high‑mortality syndrome for which prognostic assessment and risk stratification rely on critical‑illness scores such as SOFA, SAPS II, SCAI, and CSS; recent work has explored machine‑learning approaches that integrate multimodal parameters—including clinical variables, biomarkers, and imaging—to build individualized prediction models. (manuscript.docx)
放在阐述 BUN/白蛋白生物学意义的段落末尾（对应参考文献 18–20）
Elevated blood urea nitrogen has been linked to worsening renal function and higher risk of microalbuminuria in diabetes, serum albumin is a key indicator for malnutrition and prognosis with predictive value comparable to multidimensional nutritional assessments, and low albumin has been associated with increased all‑cause mortality in long‑term follow‑up. These observations provide external coherence for BAR as a composite index of renal and systemic inflammatory–nutritional stress. (manuscript.docx)
可选的细节统一建议（非翻译内容，可按需采纳）
- 在摘要与正文中统一 BAR 的定义为 blood urea nitrogen‑to‑albumin ratio，避免与 “bilirubin‑to‑albumin ratio” 混用。
- 统一“time zero/index time”表述（建议均为 ICU admission）。
- 清理个别排版与字符问题（如 “Table 2 … 加number”、杂散字母“f”、中文标点）。
- 若需要，可将本次替换内容应用“修订”模式（track changes）标注，便于协作审阅。
以上文本段落已经过语体与术语一致性处理，可直接粘贴到原文相应位置。(manuscript.docx)
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下面给出**“图表—正文一致性核对报告”与可直接替换的修订建议**。整体结论：主文对图表与表格的叙述总体正确，编号与所述内容大体匹配；仍存在若干需要修正与统一的小问题（术语、时间点定义、版式与细节标注），以及若干可提升可复现性的改进空间。相关依据均标注于文内。(manuscript.docx) (manuscript.docx) (supplementary files.docx)
一、逐项核对（Figures & Tables）
Figure 1（入排流程图）
- 正文表述：起始 3,192 例 → 依次排除 190、837、1,154 例 → 纳入 1,011 例；与图题“Flowchart of patient inclusion and exclusion”一致。
- 核对结论：** 正确**；数值与分层口径一致。(manuscript.docx)
Table 1（按 BAR 三分位的基线特征）
- 正文表述：多数一般特征与部分生命体征在三分位间无显著差异；SOFA/SAPS II 随 BAR 升高显著增加；CRRT 使用率 T1→T3 从 14% 升至 36%，ACEI/ARB/statin 随 BAR 升高而减少；与表内 P 值一致（多项 P<0.05/0.001）。
- 核对结论：** 正确**；与表格 P 值及方向吻合。建议在表注中补充三分位阈值切点以便外部复现（当前仅能由各组中位数推断）。(manuscript.docx)
Figure 2（KM 生存曲线）
- 正文表述：按 BAR T1–T3 分层，30 天即出现稳定分离（log‑rank P<0.001），并在 60/120/365 天持续；与图题 A–D 面板顺序一致。
- 核对结论：** 正确**；建议在图题或图内标注事件数与 at‑risk 数以及log‑rank P 值，增强可读性与可复现性。(manuscript.docx)
Table 2（Cox 比例风险模型）
- 正文表述：Model 3 “每 +1 mg·g⁻¹ BAR” 关联 30/60/120/365 天死亡风险增加约 2.6%–2.7%；T3 vs T1 的 HR 约 1.75–1.81，T2 vs T1 未显著；线性趋势显著。
- 核对结论：** 正确**；数值与表格条目一致（如 30 天连续变量 HR=1.026[1.011–1.041]，T3 vs T1=1.803 等）。需修正的版式问题：
- 标题中“COX”建议改为“Cox”；
- “Per 1 Unit increase”建议统一为“Per 1 mg·g⁻¹ increase”；
- 若干排版/字符错误需清理：如“120‑day mortality”行
1.037 (1.027 1.0460缺右括号，“365‑day mortality”行 T3 末尾多余字母f，标题尾部残留“加number”。 - P 值书写体例统一（建议全部用
<0.001或保留三位小数），区间用短横（–）并在 HR 与 95%CI 之间统一空格。(manuscript.docx)
Figure 3（受限三次样条，RCS）
- 正文表述：各时间点显示正向、近似线性关系，无明显阈值。
- 核对结论：** 与图题 A–D 对应关系一致**。建议在图题或方法中补充结点数/位置（如 3 或 4 结点、按分位数设定）、是否对 BAR 取对数或中心化，以及在 x 轴标注三分位刻度以便对照。(manuscript.docx)
Figure 4（30 天亚组/交互）
- 正文表述：各亚组方向一致，无显著交互（P‑interaction>0.05），并在 60/120/365 天的补充图 1–3 得到复现。
- 核对结论：** 正确**；建议在图中或图注增加每个交互检验的 P 值或统一报告“全局/关键交互”的 P 值，并在补充图中保持同一呈现规范。(manuscript.docx) (supplementary files.docx)
Figure 5（机器学习 ROC 曲线）
- 正文表述：主文图 5 为 ROC，补充图 4 为 PR（平均精确率），XGBoost 最优；叙述与方法一致。
- 核对结论：** 正确**；建议在图注或正文增加各模型平均 ROC‑AUC 与 95%CI，并在补充图 4 中给出**平均精确率（AP）**数值，避免仅凭观感比较。(manuscript.docx) (supplementary files.docx)
Figures 6–7（SHAP 排名与 Beeswarm），Supplementary Figure 7（个体力图）
- 正文表述：BAR 位列高影响特征，呈单调正向贡献；个体力图显示高 BAR 推动死亡方向。
- 核对结论：** 正确**；建议在图注注明训练/解释所用模型（如 XGBoost）、采样集（训练/验证）、以及特征重要性度量（mean |SHAP|）。(manuscript.docx) (supplementary files.docx)
Supplementary Figure 6（Boruta）
- 正文表述：Boruta 佐证 BAR 的非冗余；与 SHAP 排名相符。
- 核对结论：** 正确**；建议在图注补充shadow features 的阈值线与重复次数。(supplementary files.docx)
Supplementary Tables 1–4（缺失/正态性、按 30 天结局的基线、单变量 Cox）
- 核对结论：与正文引用一致；但 Supplementary Table 3 中“IMV”被误写为“IVW”，应统一为“IMV”。pH 的 HR≈0.002 可能反映量纲/尺度问题（按“每 1.0 pH 单位”变化计算会产生极端 HR），建议在注释中说明标准化或换算单位（如每 0.1 单位 pH）。(supplementary files.docx)
二、需立即修正的文本一致性与术语问题（可直接替换）
- BAR 的英文全称
- 问题：摘要与关键词仍将 BAR 释义为“bilirubin‑to‑albumin ratio”，应为“blood urea nitrogen‑to‑albumin ratio”。(manuscript.docx) (manuscript.docx)
- 替换：将文中所有 “bilirubin‑to‑albumin ratio (BAR)” 统一改为 “blood urea nitrogen‑to‑albumin ratio (BAR)”。
- 起始时间（index time/time zero）口径不一
- 问题：方法“Exposure”段将 index time 写为“首次入院/first hospital admission（含中文残留‘入院’）”，而“Outcomes”段与统计分析均以“ICU admission”作为 time zero。(manuscript.docx)
- 替换（建议统一为 ICU 入科时刻）：
The index time was defined as ICU admission. The primary exposure was the BAR calculated from the earliest available BUN and albumin values within the first 24 hours of the ICU stay… (manuscript.docx)
- Table 2 标题与单元格排版
- 替换：
- 标题改为：Table 2. Cox proportional‑hazards models for the association between BAR and 30‑, 60‑, 120‑, and 365‑day mortality.
- 将“Per 1 Unit increase”统一为“Per 1 mg·g⁻¹ increase”；修复缺失括号与多余字符；删除“加number”。(manuscript.docx)
- 变量名与缩写统一
- 替换/清理：将 Supplementary Table 3 的“IVW”统一为“IMV”；全篇 SpO₂/HR/SBP/DBP 及单位（mmHg、bpm、°F、mg·dL⁻¹、g·dL⁻¹、mg·g⁻¹）统一书写与空格规则。(supplementary files.docx)
三、增强可复现性与可解释性的改进（建议采纳）
- 生存/回归的关键数值显式化：在正文或图注补充 KM 曲线的 at‑risk 表、log‑rank P 值；为 Cox Model 3 提供全模型系数表（可作为新增补充表），并报告PH 假设检验的全局与各协变量 P 值。(manuscript.docx)
- RCS 设定透明化：在方法或图注写明结点数与位置、样条基函数、惩罚/拟合细节；在 x 轴标注 BAR 三分位值；在曲线下方添加“rug”展示 BAR 分布。(manuscript.docx)
- 机器学习结果量化呈现：在图 5（或补充表）列出各模型 ROC‑AUC（均值与 95%CI）；在补充图 4 配套列出平均精确率（AP）与事件率；在补充材料提供校准曲线与 Brier 分数（正文已称“包含 calibration”，但当前补充材料未见对应图—建议新增 Supplementary Figure S8）。(manuscript.docx) (supplementary files.docx)
- 分层阈值的可迁移性：在方法或表注给出 BAR 三分位 cut‑points 的确切数值，并在讨论中强调不直接将队列特异的分位点作为临床阈值，而需在外部验证基础上定义与绝对风险和净受益挂钩的阈值。(manuscript.docx)
- 用语与编号体例统一：主文统一使用 “Figure/“Table”（不混用 Fig./Fig. S 等），补充材料统一 “Supplementary Figure X / Supplementary Table X”；连字号与非断行空格的使用统一。(manuscript.docx) (supplementary files.docx)
四、可直接粘贴的微调示例（英文）
Figure 2. Kaplan–Meier curves for all‑cause mortality by BAR tertiles (T1–T3) at 30, 60, 120, and 365 days after ICU admission. Numbers at risk are shown below each panel; log‑rank tests compare survival across tertiles. (manuscript.docx)
Figure 3. Restricted cubic spline (RCS) functions depicting the adjusted association between continuous BAR and mortality at 30, 60, 120, and 365 days. Knots were placed at prespecified percentiles of the BAR distribution; shaded areas indicate 95% confidence intervals. (manuscript.docx)
Figure 5. Receiver‑operating‑characteristic (ROC) curves comparing eight supervised algorithms for 30‑day mortality prediction. Mean ROC‑AUCs (95% CIs) are reported; precision–recall curves and average precision are provided in Supplementary Figure 4. (manuscript.docx) (supplementary files.docx)
Table 2. Cox proportional‑hazards models for the association between BAR and 30‑, 60‑, 120‑, and 365‑day mortality. Estimates are hazard ratios (HRs) with 95% confidence intervals (CIs). “Per 1 mg·g⁻¹ increase” corresponds to a +1 mg·g⁻¹ increase in BAR. Models adjust sequentially as specified in the footnotes. (manuscript.docx)
五、与主文叙述密切相关的两点提醒
- 术语统一：将全文 BAR 的中文与英文定义统一为“血尿素氮/白蛋白比（blood urea nitrogen‑to‑albumin ratio, BAR）”，避免与胆红素/白蛋白比混用。(manuscript.docx) (manuscript.docx)
- 时间锚点统一：将 index time 与 time zero 统一为“ICU admission”，确保 KM、Cox 与暴露窗口（ICU 入科后 24 h 内）完全一致。(manuscript.docx) (manuscript.docx)
结语：除上述版式与口径细节外，正文对 Figure 1–7、Table 1–2 以及 Supplementary Figures/Tables 的引用与解读总体准确。完成以上修订后，图文一致性、可复现性与读者可读性将得到显著提升。(manuscript.docx) (manuscript.docx) (supplementary files.docx)