Abstract TMP95: Structured Screening for Post-Stroke Cognitive Impairment in the Outpatient Stroke Clinic
Bibliographic record
Abstract
Introduction: Cognitive impairment (CI) affects 30% of stroke survivors and impacts ability to return to work, drive and perform ADLs. However, there is no standardized screening for post-stroke CI. We implemented CI screening in the STEP (Stroke Transitions, Education and Prevention) clinic. We sought to identify demographic and clinical factors associated with early post-stroke CI. Methods: Eligible pts had ischemic stroke, ICH or TIA, were seen in the STEP clinic from March 2017 to June 2018, and included in the prospective outpatient clinical registry. Screening for post-stroke CI was performed with a Brief Neurocognitive Screen (BNS), a validated 5-minute subset of the Montreal Cognitive Assessment. BNS 0-8 was defined as abnormal (CI present) and 9-12 was defined as normal. Continuous variables were analyzed with student t-tests or Wilcoxon rank-sum tests and categorical variables with Fisher’s exact test. Logistic regression was performed with the significant variables in the univariate analyses. Results: Of 256 patients, 116 completed a BNS at a median of 35 days after hospital discharge. Median NIHSS was 3 (IQR 0.5,6) and follow-up modified Rankin scale (mRS) was 1 (IQR 1,2). Median BNS was 10 (IQR 9,11). Abnormal BNS, was present in 17.2% of pts screened. Of the 20 pts with abnormal BNS, 17 had neuropsychological testing ordered. In the univariate analysis, age, education, admission NIHSS, poor mRS (<2) at follow-up, and atrial fibrillation were significantly associated with early post-stroke CI (Table 1). In the multivariable analysis, only age and follow-up mRS remained significant. Conclusion: Early post-stroke CI is common in stroke pts, even with low NIHSS, and associated with older age and worse mRS. The BNS is a post-stroke CI screening tool than can be performed in stroke clinics. Future studies are needed to assess the feasibility of implementing the BNS across multiple sites and outcomes associated with early identification of post-stroke CI.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".