Application of Shouwu Yizhi prescription in decubation of patients with ischemic stroke
Bibliographic record
Abstract
We investigate the application of Shouwu Yizhi prescription (SYP) in decubation of patients with ischemic stroke (IS). The clinical data of 106 patients recovering from IS who came to our hospital from December 2019 to December 2020 were selected for retrospective analysis, and they were separated into experimental group (n = 53, basic treatment + SYP) and control group (n = 53, basic treatment) based on the principle of random grouping. The clinical indexes such as lipid indexes and neurological disability score (NDS) after treatment were compared between both groups to comprehensively evaluate the clinical effects of different treatment regimens. Except for high-density lipoprotein cholesterol value, the lipid indexes in the experimental group after treatment were remarkably lower than those in the control group (P < 0.001). After treatment, the levels of hypersensitive C-reactive protein, homocysteine and lipoprotein-associated phospholipase A2 were remarkably lower in the experimental group than control group (P < 0.05). After treatment, the experimental group had remarkably higher mean scores of Montreal Cognitive Assessment, Fugl-Meyer Assessment in upper and lower limbs and lower NDS than control group (P < 0.001). SYP is an efficient treatment plan in decubation of IS, which can effectively improve the blood lipid indexes and neurological function of patients, and further studies will help establish a better solution for such patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 source (direct Gemma or distilled Codex), 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".