PT566. A survey of medication using antipsychotics in patients with dementia.
Bibliographic record
Abstract
Introduction:Recently, the pharmacological treatment has aimed to improve cognitive deficits and behavioral and psychological symptoms of dementia.Antipsychotics are often used for excitement or hyperactivity of dementia and evaluating the efficiency of the medication has been important.We researched the tendency of choice of antidementia drugs and antipsychotics for patients with dementia.Materials: This study included 96 patients with dementia admitted to Showa University Northern Yokohama Hospital from January 2014 to August 2015 (average age 80.1 ± 8.5 years, average duration of hospitalization 130.8 ± 79.2 days; 37 males and 59 females).77 were diagnosed as Alzheimer disease, 14 as dementia with Lewy bodies and 5 as other types of dementia.Methods: We investigated the severity of psychological symptoms on admission and medication for the symptoms.We compared psychological symptoms and The global Assessment Functioning on admission and the use of antipsychotics of patients treated with antidementia drugs with those of patients without antidementia drugs retrospectively by clinical records.This study was approved by the Ethics Committee of Showa University Northern Yokohama Hospital and we considered personal information protection fully.Result: 66 patients treated with antidementia drugs had more severe excitement or agitation?compared to 47 patients without antidementia drugs.72.7% (48/66) patients received more than two drugs, quetiapine was most used (24.2%, 16/66) and perospirone was secondary used (16.7%, 11/66).Quetiapine tended to aim to improve excitement or agitation for patients with less duration of illness and higher score of GAF.Aripiprazole was most used for patients treated without antidementia drugs.Conclusion: In this study, antidementia drugs were used toward excitement or agitation, and antipsychotics were added for mood stabilizing or sedation.The study suggested that it is effective to select medication according to characteristics of behavioral and psychological symptoms.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".