Efficacy of donepezil in patients with cognitive dysfunction caused by radiation-induced encephalopathy
Bibliographic record
Abstract
Objective To evaluate the efficacy and safety of donepezil in the treatment of cognitive dysfunction caused by radiation-induced encephalopathy. Methods A total of fifty-five patients with radiation-induced cognitive impairment were divided into treatment group with extra donepezil 5-10 mg/d combined with conventional therapy and control group with conventional treatment for 16 weeks. The cognitive function was assessed according to Montreal cognitive assessment (MoCA) and mini-mental state examination (MMSE) before and 16 weeks after treatment. Results After 16 weeks of treatment, the patients in treatment group displayed significantly greater improvement in cognitive function. In treatment group, the scores of patients after donepezil therapy in MoCA and MMSE were obviously higher than the control group (t=5.40, 3.88, P<0.01). The scores in the visual space and executive function, naming, attention, abstract thinking, delayed memories also had improved, which suggested the statistically significant difference(t=-3.55, -3.08, -3.21, -2.58, -3.65, P<0.05). The scores of control group unchanged accordingly. Conclusions Donepezil combined with conventional treatment was signally effective in the therapy of cognitive dysfunction caused by radiation-induced encephalopathy. Key words: Radiation-induced encephalopathy; Cognitive dysfunction; Donepezil
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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 | 0.000 |
| 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) | 0.000 | 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".