A Nationwide Multi-Center Questionnaire Survey on the Real-World State and Clinical Management of Poststroke Dementia in Japan
Bibliographic record
Abstract
BACKGROUND: Poststroke dementia (PSD) is a serious problem for stroke survivors. However, there is still limited data on the real-world state and clinical management of PSD worldwide, and several countries already have a super-aged society. OBJECTIVE: We conducted a nationwide questionnaire survey to examine the real-world state and management of PSD in Japan. METHODS: A survey was conducted in the top 500 Japanese hospitals regarding the number of stroke patients treated between July 2018 and August 2019. Thirteen questions regarding PSD were mailed to doctors responsible for stroke management. RESULTS: Responses were obtained from 251 hospitals (50.2%). The chief doctors responsible for stroke management answered the questionnaires. The median numbers of patients admitted annually with stroke in the departments of neurology and neurosurgery in the hospitals were 281.0 (interquartile range [IQR], 231.8-385.3) and 253.5 (IQR, 210.0-335.3), respectively, and most hospitals were acute care hospitals. Executive dysfunction was the most common cognitive dysfunction (10.9%), followed by amnesia (9.5%) and apathy (4.1%). Surprisingly, many stroke survivors lived alone at home (23.7%). Montreal Cognitive Assessment was significantly uncommon compared to Mini-Mental State Examination (p < 0.01). Furthermore, objective evaluation tests for behavioral and psychological symptoms of dementia were not often performed. Cognitive rehabilitation treatments were performed more often and earlier than drug treatments. The first drug of choice for PSD was predominantly donepezil (79.1%), followed by galantamine (6.1%), cilostazol (4.9%), memantine (2.5%), and rivastigmine (1.8%). CONCLUSION: Our study provides real-world evidence for the state of clinical practice related to PSD in Japan.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".