Observations on the Efficacy of Lower Point Selection for Upper Disease in Treating Cognitive Impairment after Acute Cerebral Infarction
Bibliographic record
Abstract
Objective To investigate the clinical efficacy of lower point selection for upper disease in treating cognitive impairment after acute cerebral infarction.Methods one hundred patients with cognitive impairment after acute cerebral infarction were randomly allocated to treatment and control groups,50 cases each.Both groups were treated with nimodipine.The treatment group received acupuncture additionally.Results The total efficacy rate was 84.0% in the treatment group and 68.0% in the control group.The difference between the two groups was statistically significant(P0.05).There was a statistically significant difference in the post-treatment Montreal Cognitive Assessment(MoCA) score between the treatment and control groups(P0.05).Conclusion Lower point selection for upper disease is an effective way to treat cognitive impairment after acute cerebral infarction.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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 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".