Methodological Factors in Determining Risk of Dementia After Transient Ischemic Attack and Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Cognitive assessment is recommended after stroke but there are few data on the applicability of short cognitive tests to the full spectrum of patients. We therefore determined the rates, causes, and associates of untestability in a population-based study of all transient ischemic attack (TIA) and stroke. METHODS: Patients with TIA or stroke prospectively recruited (2002-2007) into the Oxford Vascular Study had ≥1 short cognitive test (mini-mental state examination, telephone interview of cognitive status, Montreal cognitive assessment, and abbreviated mental test score) at baseline and on follow-up to 5 years. RESULTS: Among 1097 consecutive assessed survivors (mean: age/SD, 74.8/12.1 years; 378 TIA), numbers testable with a short cognitive test at baseline, 1, 6, 12, and 60 months were 835/1097 (76%), 778/947 (82%), 756/857 (88%), 692/792 (87%), and 472/567 (83%). Eighty-eight percent (331/378) of assessed patients with TIA were testable at baseline compared with only 46% (133/290) of major stroke (P<0.001). Untestability was also associated with older age, premorbid dependency, death on follow-up, and with both pre- and postevent dementia (all P<0.01). Untestability (and problems with testing) were commonly caused by acute stroke effects at baseline (153/262 [58%]: dysphasia/anarthria/hemiparesis=84 [32%], drowsiness=58 [22%], and acute confusion=11 [4%]), whereas sensory deficits caused relatively more problems with testing at later time points (24/63 [38%] at 5 years). CONCLUSIONS: Substantial numbers of patients with TIA and stroke are untestable with short cognitive tests. Future studies should report data on untestable patients and those with problems with testing in whom the likelihood of dementia is high.
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 | 0.023 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".