Abstract WP66: Acute Stroke Screening For Cognitive Disorders And Depression
Bibliographic record
Abstract
Introduction: As stroke survivors transition from acute to post-acute care, and finally to community settings, the Centers for Disease Control reports ~65% receive NO rehabilitation. Even more receive rehabilitation too late, after critical brain changes for recovery are complete. Stroke survivors with invisible disabilities of cognition and depression are especially vulnerable to experience poor recovery. We launched a dedicated process to identify invisible disabilities. Our long-term objective is to make acute and post-acute, evidence-based intervention accessible. Hypothesis: >50% of acute stroke patients have cognitive deficits, or depression. Methods: Our comprehensive stroke center completes bedside psychometric assessment with standardized instruments for aphasia (Language Screening Test, LAST), spatial neglect (Catherine Bergego Scale, CBS), memory/global cognition (Montreal Cognitive Assessment, MoCA), delirium (3-Minute Diagnostic Interview for the Confusion Assessment Method, 3D-CAM) and depression (Patient health questionnaire, PHQ-8). Patients unable to respond to questions are assessed for spatial neglect and delirium (standardized observations). Results: 105 ischemic stroke survivors were assessed in the first quarter of program launch (April-July, 2021). Of that group, patients met screening criteria for spatial neglect (47%), aphasia (40%), delirium (19%) and depression (31%). Over 90% had memory / global cognitive impairment (MoCA<26/30). Conclusions: Our initiative, which includes systematic acute stroke unit spatial neglect screening, confirmed the previously reported high rate of cognitive disorders and depression (Champod, Eskes, Barrett, 2020). Our current step implements uniform recommendations for patients with deficits, and will examine post-acute outcomes, number receiving rehabilitation and medical follow-up, and treatment disparities (right/left stroke, under-represented groups).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".