Bibliographic record
Abstract
目的探讨初发急性脑梗死（ACI）恢复程度影响因素，为可控性干预措施提供指导。方法选取2022年1月至2022年12月在南阳市中心医院接受静脉溶栓治疗的初发ACI患者362例为研究对象，治疗后3个月采用改良Rankin量表（mRS）评估患者恢复程度，分析不同特征患者mRS评分情况，Pearson法分析mRS评分与汉密尔顿焦虑及抑郁量表（HAMA、HAMD）评分的相关性，多元回归分析初发ACI患者恢复程度的影响因素，采用部分标准化方法对各影响因素进行重要性排序。结果治疗后3个月，362例初发ACI患者mRS评分（2.36±0.83）分。Pearson法分析显示，mRS评分与HAMA评分、HAMD评分存在明显相关性（<italic>r</italic>=0.805，<italic>r</italic>=0.738，均<italic>P</italic><0.05）。多元回归分析显示，年龄、静脉溶栓时间、入院时NIHSS评分、血流再通良好、血小板/淋巴细胞比率（PLR）水平、严重脑水肿、HAMA评分、入院时Alberta卒中操作早期急性卒中分级CT（ASPECT）评分、中性粒细胞/淋巴细胞比率（NLR）水平、HAMD评分是初发ACI患者mRS评分的影响因素（<italic>P</italic><0.05）。结论初发ACI患者恢复程度受焦虑、抑郁情绪等多因素影响，改善患者情绪状态，可促进患者预后恢复。
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.094 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".