Abstract NS11: Identifying Discharge Needs for Stroke Patients Using the Montreal Cognitive Assessment
Bibliographic record
Abstract
Background: Patients who develop a stroke are at high risk for cognitive decline. The Montreal Cognitive Assessment (MOCA) is a validated tool for assessing cognitive function within this patient population prior to discharge. An important limitation of the MOCA is that often times there is no pre-morbid score for comparison. Despite this, consideration of occupational and cognitive therapy is important in this population because of the high risk for cognitive decline. Methods: A total of 231 patients were treated on the stroke unit from December 2015 to June 2016, of these 149 patients were excluded due to activity intolerance, severe communication barriers, patient refusal, or cognitive deficits exceeding the limit of the screening tool. A retrospective chart review was conducted on the 82 patients and data on demographics, stroke risk factors, stroke type (ischemic, subarachnoid hemorrhage, or intracerebral hemorrhage), MOCA scores, and discharge disposition (home or acute/subacute rehabilitation) was extracted. Results: Of the 82 patients in the study, 45 (55%) were male, with an average age of 54.9 years. Thirty-eight (46%) of these patients suffered hemorrhagic strokes. Average MOCA scores for hemorrhagic stroke patients who were either discharged home versus a rehab setting were similar when compared to the ischemic stroke population (20.1 and 18.2 versus 21.3 and 16.9, respectively). However, when subcategorized by stroke subtype, the subarachnoid hemorrhage population exhibited higher MOCA scores for those who were discharged home or to rehab versus the patients with ICH who were discharged home or to rehab (21.9 and 25.4 versus 19.1 and 13.6, respectively). Interestingly, the patients within the SAH cohort who were discharged to rehab had a higher MOCA average than those who were discharged home. Conclusion: Many patients who suffer hemorrhagic strokes in this population are discharged home despite having a MOCA score below normal (<26). This may demonstrate residual cognitive deficits as a result of their disease process. Despite a patient’s ability to be functionally and physically able to return home, there is an identified need for addressing cognitive deficits in order to improve quality of life.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".