The Effect Of Pentoxifylline On Blood Biomarkers In Patients With Cerebral Infarction Combined With Senile Debilitating Syndrome: Implications For Prognosis
Bibliographic record
Abstract
Cerebral infarction combined with senile debilitating syndrome seriously affects the quality of life of patients, this study analyzed the effect of pentoxifylline on the expression of blood biomarkers in patients with this disease and its relationship with prognosis. 100 patients with cerebral infarction combined with senile debilitating syndrome admitted to our hospital from December 2022 to December 2024 were divided into control and study groups, both groups were treated with basic therapy, and study group were additionally treated with pentoxifylline. The neurological deficit degree (NIHSS score) and Montreal Cognitive Assessment (MoCA) score, coagulation function indicators, blood rheology indicators, inflammatory indicators, clinical efficacy and adverse reactions were analyzed. The relationship of blood biomarker expression with prognosis was assessed using logistic regression analysis and receiver operating characteristic (ROC) curve. After treatment, all indicators in both groups were superior to the pre-treatment (P<0.05). NIHSS score, haematological indicators, inflammatory indicators and adverse reaction incidences of study group were below to control group, MoCA score, coagulation function indexes and clinical efficacy were above to control group (P<0.05). Logistic regression and ROC results showed the effect of blood biomarker expression and prognosis were remarkably correlated. The treatment is efficacious and worthy of clinical promotion.
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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) | 0.000 | 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 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".