Abstract WP392: Reducing Readmissions through Secondary Stroke Prevention Clinics
Bibliographic record
Abstract
Introduction: Secondary Stroke Prevention Clinics (SPCs) were established in Ontario to provide the opportunity for appropriate patients to access best practice stroke care approaches to reduce the risk of recurrent stroke/TIA. Previous evaluations have shown that SPCs provide high quality care based on evidence-based performance measures; however the impact of SPCs on readmission rates has not been examined. Information on the proportion of patients who are readmitted to hospital for stroke/TIA after being seen at an SPC is important to understanding the role of SPCs in preventing stroke/TIA in Ontario. Methods: The Ontario Stroke Registry’s SPIRIT-SPC database was used to identify patients with presumed stroke/TIA referred to 13 SPCs between January 2007 and April 2011. Patients with a first clinic visit and an index stroke/TIA event between January 2007 and September 2010 were linked to Canadian Institute of Health Information (CIHI) inpatient database to calculate stroke-related readmission rates. Patients with elective admissions were excluded. The rates were based on the four years of data and were age- and sex- adjusted. Results: The study included 15,163 patients from SPIRIT-SPC seen at 13 SPCs in Ontario. The mean age was 66 years (±14) and 49.3% of patients were female. Seventy-eight percent (n= 11,777) of patients had an index stroke/TIA event reported on the SPC referral and of these only 60% (n = 9,040) had an ED visit or inpatient admission record in the CIHI databases capturing the index stroke/TIA event. The median time from referral to initial visit was 11 days. The stroke/TIA 30-, 90- and 180-day readmission rates following the SPC first visit were 0.7%, 1.3% and 1.8%, respectively. Variation in readmission rates across 13 SPCs ranged from 0 - 6%. Conclusion: Stroke/TIA-related readmission rates among patients seen at the 13 SPCs in Ontario patients are lower than what is reported in the literature and lower than observed among patients discharged from acute hospitals following a stroke/TIA ED visit or in-hospital stay. The lower rates observed at the 13 clinics may reflect less severe patients being seen at SPCs. Risk-adjusted models will be taken into consideration for future studies.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".