050 Feasibility of an automated assessment to measure cognition and mood in the acute stroke setting
Bibliographic record
Abstract
Introduction Over 50% of stroke survivors have cognitive impairment. National guidelines promote early cognitive testing however, current pen-and-paper based tests are not always appropriate, typically take place in hospital and are time costly for busy clinicians. This project aimed to create an easy-to-use cognitive assessment tool specifically designed for the needs of stroke survivors. We used a computerised doctor utilising automatic speech recognition and machine learning. Methods Patients are approached if they pass the eligibility criteria of having recent acute stroke/TIA, and do not have pre-existing condition i.e dementia, severe aphasia Participants could speak to the digital doctor on the ward or at home via a web-version. Results Recruitment started on 8th December 2020; We have screened 614 people assessed for suspected acute stroke/TIA at Sheffield Teaching Hospitals. Of those we have recruited 71 participants (13 with TIA) Mean NIHSS of 4.5 and mean MoCA of 24.6. We will present initial results of factors affecting participant recruitment. We will also compare the mood and anxiety screening scores used in this study to those collected via the SNAPP database. Discussion Screening was adapted due to Covid pandemic and utilising remote consent and participa- tion allowed the project to continue.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".