Causal Analysis of Social Support on Functional Outcomes in Stroke Rehabilitation: A Study Using Synthetic Data and Advanced Inference Methods
Bibliographic record
Abstract
Abstract The role of social support in the recovery of stroke survivors has been a subject of considerable interest. It has been hypothesized that social support may positively influence functional outcomes through various mechanisms, including enhanced adherence to rehabilitation programs, reduced psychological stress, and improved overall well-being [1]. However, establishing a clear causal link between social support and functional recovery in stroke patients is challenging due to the presence of numerous confounding factors that can influence both the level of social support received and the extent of functional improvement. This study aimed to investigate the causal effect of high social support on functional outcomes in stroke rehabilitation patients, utilizing advanced causal inference methods applied to a large synthetic healthcare dataset. The synthetic data, generated to mirror real-world patterns while preserving patient privacy, allowed for a rigorous analysis that addressed potential confounding. The key finding of this analysis was that, after adjusting for a comprehensive set of confounders, including depression, high social support showed no significant causal effect on functional outcomes 12 months post-stroke. This result contrasts with initial unadjusted analyses that indicated a significant positive association, highlighting the critical importance of accounting for confounding in observational studies. The emergence of depression as a key confounder suggests that psychological factors may play a more significant role in rehabilitation outcomes than social support alone. These findings have important implications for the design of interventions aimed at improving functional recovery after stroke, suggesting that a focus on addressing psychological well-being alongside social support may be more effective.
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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 | 0.043 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".