Abstract WP429: The Cognitive Impairment in Stroke Screener (CISS) Tool: an Improved Screening Tool to Detect Cognitive Impairment Early Among Stroke Patients
Bibliographic record
Abstract
Background: Early cognitive screening of stroke patients may identify unmet rehabilitative needs during stroke recovery but validated tools are lacking. We report our experience with the Six Item Screener (SIS) compared to the Montreal Cognitive Assessment (MOCA) and attempt to improve upon its shortcomings in stroke patients. Hypothesis: Low sensitivity of the SIS for cognitive impairment (CI) in stroke patients can be improved by incorporating visuoexecutive dimensions. Methods: Patients admitted with ischemic stroke (IS), transient ischemic attack (TIA), intracerebral (ICH) or subarachnoid hemorrhage (SAH) between December 2014 and June 2015 underwent inpatient screening for CI using SIS and MOCA, administered by speech-pathologists if they were alert and not aphasic. Predictive value and sensitivity/specificity cut-offs of SIS for CI (MOCA≤23) were determined. A screening tool, created by adding a clock-drawing task and dropping least important SIS components was developed. Results: Of 110 (IS/TIA: 56, ICH: 17, SAH: 26) patients who had MOCA and SIS performed at the same visit, 79 patients had CI; other patient characteristics including stroke severity, are described (Fig 1A). The AUC of SIS for CI was 0.78 and comparable across stroke types (AUC for IS: 0.81 ICH: 0.79 SAH: 0.95). SIS≤4 had 46.6% sensitivity for CI while SIS≤5 had 72.6% sensitivity and 80.6% specificity for CI. Excluding ‘year’ and ‘month’ questions of the SIS had no effect on the performance of the screening test (AUC=0.78 without). A 7-item tool (CISS) that included the clock drawing task (3 points) and omitted “year/month” SIS questions had excellent predictive power for CI (AUC=0.89) and comparable across stroke types (AUC 0.89-0.93) (Fig 1B,C). CISS≤6 had 94.5% sensitivity and 49.4% specificity for CI. Conclusions: The CISS improves upon the low sensitivity of the SIS for CI in stroke patients. A validation study using 3-month neuropsychological testing as the gold-standard is underway.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".