OP75 The potential impact of cognitive rehabilitation on the future burden of post-stroke cognitive impairment in Ireland to 2035: Preliminary results using a model-based approach
Bibliographic record
Abstract
Background Post-stroke cognitive impairment (PSCI) is a frequent consequence of stroke, and reduces quality of life and increases care needs. We aimed to evaluate the impact of a hypothetical cognitive rehabilitation intervention on PSCI outcomes using the StrokeCog epidemiological model. Methods We developed a probabilistic Markov model to project and track incidence and prevalence of PSCI in the Irish population aged 40–89 years to 2035. Data sources included official population and hospital episode statistics, and longitudinal cohort studies. Drawing on available systematic review evidence, we hypothesized that cognitive rehabilitation would reduce the risk of cognitive impairment no dementia (CIND) at 1 year post-stroke by 18% (scenario 1, S1, small effect) or by 54% (scenario 2, S2, medium effect) relative to usual care. Results In usual care, the projected prevalence of post-stroke CIND in Ireland in 2035 was 6.7 per 1000 general population (95% CI 5.6–7.8), or 35% of stroke survivors (95% CI 30.5–38.8) (n=21026 prevalent cases). In S1 (small effect) the projected prevalence was reduced to 32.0% (95% CI 28.6–36.4) of stroke survivors (n=19652), and in S2 (medium effect) to 29.1% (95% CI 25.2–33.2) of stroke survivors (n=17672). The number of years of life lived free of cognitive impairment were increased by 6.3% in S1 (small effect) and 15.1% in S2 (medium effect). Conclusion The StrokeCog model provides a tool for policy-makers and researchers to evaluate the potential impact of cognitive rehabilitation at different levels of intervention effectiveness. The model was based on conservative assumptions, and a less conservative approach could lead to a greater projected reduction in burden. Our next steps include analysis of quality of life outcomes and costs.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.013 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".