The impact of stroke, cognitive function and post-stroke cognitive impairment (PSCI) on healthcare utilisation in Ireland: a cross-sectional nationally representative study
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment after stroke is associated with poorer health outcomes and increased need for long-term care. The aim of this study was to determine the impact of stroke, cognitive function and post-stroke cognitive impairment (PSCI) on healthcare utilisation in older adults in Ireland. METHODS: This cross-sectional study involved secondary data analysis of 8,175 community-dwelling adults (50 + years), from wave 1 of The Irish Longitudinal Study on Ageing (TILDA). Participants who had been diagnosed with stroke by a doctor were identified through self-report in wave 1. Cognitive function was measured using the Montreal Cognitive Assessment (MoCA). The main outcome of the study was healthcare utilisation, including General Practitioner (GP) visits, emergency department visits, outpatient clinic visits, number of nights admitted to hospital, and use of rehabilitation services. The data were analysed using multivariate adjusted negative binomial regression and logistic regression. Incidence-rate ratios (IRR), odds ratios (OR) and 95% confidence intervals (CI) are presented. RESULTS: The adjusted regression analyses were based on 5,859 participants who completed a cognitive assessment. After adjusting for demographic and clinical covariates, stroke was independently associated with an increase in GP visits [IRR (95% CI): 1.27 (1.07, 1.50)], and outpatient service utilisation [IRR: 1.49 (1.05, 2.12)]. Although participants with poor cognitive function also visited the GP more frequently than participants with normal cognitive function [IRR: 1.07 (1.04, 1.09)], utilisation of outpatient services was lower in this population [IRR: 0.92 (0.88, 0.97)]. PSCI was also associated with a significant decrease in outpatient service utilisation [IRR: 0.75 (0.57, 0.99)]. CONCLUSIONS: Stroke was associated with higher utilisation of GP and outpatient services. While poor cognitive function was also associated with more frequent GP visits, outpatient service utilisation was lower in participants with poor cognitive function, indicating that cognitive impairment may be a barrier to outpatient care. In Ireland, the lack of appropriate neurological or cognitive rehabilitation services appears to result in significant unaddressed need among individuals with cognitive impairment, regardless of stroke status.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".