The Predictive Value of Serum Alkaline Phosphatase Level on the Degree of Neurological Impairment in Patients with Acute Cerebral Infarction
Bibliographic record
Abstract
Objective: To analyze the correlation between the changes of ALP level and the degree of neurological impairment in patients with acute cerebral infarction. Methods: A total of 267 patients with acute cerebral infarction were selected as the cerebral infarction group, 181 elderly patients who were matched age and gender in the same period with the cerebral infarction group were selected as the control group by the physical examination. All the selected patients were tested for serum ALP, ALT, AST, Cr, BUN,TG, TC, LDL - C and HDL - C after eight hours on an empty stomach. In the 72nd hour of the patient's course of cerebral infarction, the degree of neurological impairment was assessed using NIHSS score. The relationship between serum ALP level and NIHSS score was analyzed. Results: According to NIHSS score, the patients with score of 5~15 were Group A , patients with score of 15~20 were Group B and score of 21~42 were Group C. Pearson correlation analysis showed that the serum ALP level of the three groups was positively correlated with NIHSS score. Multivariate regression analysis results showed that the high serum ALP level of the risk of cerebral infarction is a low serum ALP level 1.58 times. Conclusions Serum ALP level was increased in patients with acute cerebral infarction and was closely related to the degree of neurological impairment. Perhaps the serum ALP level may be used as a serum marker to predict the degree of neurological impairment in 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".