Abstract WP365: STARS PLUS Patient Transition Pilot Program
Bibliographic record
Abstract
Background: As the leading cause of adult disability and the fourth leading cause of death in the United States, stroke prevention strategies are imperative. Arguably equal attention should be given towards both primary and secondary prevention of stroke. While much is known about medications and lifestyle changes to prevent recurrent stroke, additional research is needed to effectively ensure stroke survivors are following the recommended guidelines. Research shows that support after discharge from the hospital post-stroke is frequently inadequate. Purpose: The purpose of the Steps Against Recurrent Stroke (STARS) Plus Patient Transition Pilot Program was to design and deliver a program to facilitate optimal recovery for stroke survivors and prevent recurrent stroke. The program began at discharge from the hospital and continued through the first year of rehabilitation and recovery. Patients could opt in based on perceived benefits of support with medication management, timed educational mailings and calls about stroke recovery and recurrent stroke prevention. Results: Twelve hospitals participated; 261 patients enrolled and contact was established with 193. Outcomes were gathered based on patient self-report of health status using the Standard Form 12 (SF-12) Health Survey at 30, 90, 180 and 360 days. A total of 72 patients completed each follow-up in the full 12 months of the program. A dependent sample t-test was completed comparing participants’ 30 and 360 day follow-up scores. Results demonstrated an overall increase in subjective pain. A repeated MANOVA was conducted to compare 30 and 360 day SF-12 scores across age and subscales. Results revealed that those in the younger and older age groups reported poorer health outcomes. Conclusion: STARS Plus Program found no statistically significant change in perceived health benefits, although the majority found the program to be beneficial. Future programs should consider targeting pain management in all ages and education targeted at younger and older age groups, as they reported poorer health outcomes. The findings from this program should contribute to the guidance and insight for others developing transitional interventions for stroke survivors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".