Abstract WP141: Identifying Modifiable Predictors of Long-term Functional Outcome After Stroke
Bibliographic record
Abstract
Introduction: Disability is often an assumed and accepted consequence of stroke. Post-stroke disability is frequently attributed to demographic risk factors such as age and stroke severity. These factors cannot explain all the variability in stroke outcomes. Other factors, such as post-stroke depression, sleep apnea and cognitive impairment can impact function, and yet their relationships to long-term outcomes are rarely assessed. The primary purpose of our research is to understand the role of these potentially modifiable factors in predicting long-term post-stroke functional outcomes. Hypothesis: Stroke patients who screen positive for depression, sleep apnea or cognitive impairment at baseline will have significantly worse long-term functional outcome. Methods: A follow up outcome assessment of stroke patients is being conducted by telephone 2-3 years after an initial baseline visit where their risk of depression, sleep apnea and cognitive impairment was assessed. Baseline predictors such as age and stroke severity are also abstracted from their baseline visit. Assessment measures were selected to evaluate numerous levels of human functioning and include the following: modified Rankin Scale, MoCA, Barthel Index, Frenchay Activites Index and Reintegration to Normal Living Index. The primary outcome is mRS Score, with a score ≥ 2 indicating poor outcome. Results: Seventy six patients have been enrolled in our study and projected enrolled of another 100 patients should be complete by December 2015. Based on preliminary data, our prognostic logistic regression model including only stroke severity and age is statistically significant, χ2(2)= 29.06, p < 0.001. This model explains 42.7% (Nagelkerke R2) of the variance in long-term outcomes and correctly classifies outcome in 78.9% of patients. Future analyses with the full sample size and addition of potentially modifiable factors will verify whether these factors increase the predictive value of our prognostic model. Conclusion: By identifying modifiable factors related to poor functional outcomes, this study may allow the development of novel interventions to alter the trajectory of this vulnerable population to help optimize long-term function after stroke.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.008 | 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".