Short-term Montreal Cognitive Assessment predicts functional outcome after endovascular therapy
Bibliographic record
Abstract
Background: The previous studies have shown that cognition in patients 4-8 weeks after stroke can predict early functional outcomes after stroke. The analyses of data from the REVASCAT trial proved that stent thrombectomy improves post-morbid wiring test outcomes in patients with AIS compared with drug therapy. However, few studies focus on the relationship between cognitive impairment and functional outcomes in patients undergoing endovascular treatment. Methods: A total of 647 participants registered from stroke centers. Stroke severity was evaluated by National Institutes of Health stroke scale (NIHSS). The functional status was estimated by modified Rankin scale (mRS). The cognitive impairment was assessed by trained neurologists at 14 (±4) and 90 (±7) days after stroke onset using the Montreal Cognitive Assessment (MoCA). A MoCA score of less than 26 was considered post-stroke cognitive impairment (PSCI). Results: < 0.001). Receiver operating characteristic (ROC) curve indicated that 14-day MoCA score, memory, abstraction, visuospatial/executive functions, attention, and language, played a significant role to predict PSCI [area under the curve (AUC) > 0.7]. It had predictive value for the 14-day visuospatial/executive functions to predict 3-month functional outcomes. Conclusion: Early application of the MoCA in different cognitive regions could predict the PSCI and future functional outcomes, which is necessary to screen high-risk patients with poor prognosis and conduct an early intervention.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".