Abstract TP380: Improving Outcomes for Stroke Patients at Risk for Cognitive and Mood Impairments
Bibliographic record
Abstract
Background: Improved survival rates of stroke patients have resulted in a rise in disability within this population. Research demonstrates that stroke patients are at high risk for cognitive decline and depression. Neuropsychological intervention can improve outcomes for this population. At an academic medical center in the Midwest, the process in which stroke patients are screened for these impairments and subsequently referred to a neuropsychologist is ineffective. Purpose: The purpose of this quality improvement project was to critically appraise the process in which stroke patients are screened for cognitive decline and depression and to improve the process using a multi-disciplinary approach of nursing, medicine, rehabilitation and neuropsychology. Methods: A total of 231 patient charts were reviewed in this quality improvement project. The Plan-Do-Study-Act model was utilized. Process changes included: provider education on order placement of neuropsychology referrals, occupational therapist education on correct progress note use, and improvement of visibility of the stroke patient list to screening staff. Pre- and post-intervention data were examined to assess for changes in screening compliance and consultations. Results: Baseline data collected December 2016 showed 64% compliance with Montreal Cognitive Assessment (MoCA) screening, 50% compliance with Patient Health Questionnaire (PHQ-2) screening, and 50% compliance with neuropsychology referral. After new processes were implemented, April 2016 data showed 100% MoCA compliance, 95% PHQ-2 compliance, and 100% neuropsychology referral compliance. Although these numbers look promising, we will continue to gather and analyze data to ensure this positive compliance trend continues. Conclusion: Multidisciplinary education and increased visibility of stroke patients requiring a screening may increase compliance of cognitive decline and depression screening as well as subsequent referral to neuropsychology. The increase in screening compliance will ultimately lead to appropriate referrals and further resources for the stroke population.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".