Abstract TP172: Global Insight into Long-term Stroke Outcomes Using the Post Stroke Checklist
Bibliographic record
Abstract
Objectives: There are long-term consequences following an individual suffering from a stroke. Despite clinical recommendations, there remains an absence of continued follow-up support for patients in most regions of the world. Estimates in the current literature estimate that 50% of patients are discharged without rehabilitation input and within two weeks, 50% of patients report having unmet needs. The Post Stroke Checklist (PSC) was designed for the long-term surveillance of continuing difficulties post stroke. This study aimed to evaluate international patterns of unmet needs following stroke, using the PSC. Methods: The PSC (endorsed by the World Stroke Organization and incorporated into the Canadian Stroke Best Practice Recommendations) identifies common long-term problems after stroke and appropriate referral pathways to address them. The PSC has been administered internationally including publications from Italy, Singapore and the UK’s stroke populations. This study follows this succession and trialed the PSC in an Australian (n=112) and Chinese (n=97) population at six months post stroke. Results: Between the samples (n=209), adjusted comparisons found a significantly higher proportion of unmet needs in China (mean= 4.67) compared with Australia (mean= 3.49). On the PSC, the most common unmet needs for both countries were similar including: absence of information regarding secondary prevention of stroke, cognitive difficulties and difficulties participating in life activities. The proportions affected on the PSC items differed between the two countries; with significantly greater difficulties in daily living activities, mobility, mood changes for China (p<.05). In Australia, greater incidences were reported for spasticity, incontinence (p<.05) and communication. The paper will also contrast these results with the other published international studies. Conclusion: Recording long-term functional disability post stroke highlights specific unmet needs and can encourage better support for community follow-up from governments, funding agencies and clinical programs. Providing international perspectives on these outcomes can also inspire changes in the post-hospital phase where pathways for treatment remain disjointed.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".