Abstract T MP79: Baseline disparities and Outcomes in Public vs Private US Health Care Institutions: The SPS3 Trial
Bibliographic record
Abstract
BACKGROUND: For optimal secondary stroke prevention timely access to medical care is paramount. Disparities in stroke care related to race-ethnicity, socioeconomic status and health care access are present. It is unclear if these variables are modified within a clinical trial setting. Our aims were to compare baseline demographic, clinical and neuroradiological features and clinical outcomes (recurrent stroke, death and myocardial infarction (MI)) in US participants in the Secondary Prevention of Small Subcortical Strokes (SPS3) trial from public vs private institutions. METHODS: There were 1677 participants from US sites. We characterized sites as “public” or “private” based upon for-profit status and source of operational funding. Baseline demographic, clinical and neuroradiological features between groups were compared using Chi-square and unpaired t-tests, as appropriate. Cox proportional hazard models were used to compare risk of outcomes between groups in crude and adjusted models including age, sex, hypertension, diabetes, smoking, dyslipidemia, ischemic heart disease, body mass index and race-ethnicity as co-variates. RESULTS: Of the 1677 US patients, 1009 (60%) were enrolled at public and 668 (40%) at private institutions. There were significant differences in public vs. private groups with respect to age (60 vs 64 y, p=0.001), race-ethnicity (59% non-white vs. 24%, p <0.0001), and educational status (15% with <9 y of education vs 4%, p<0.0001). Antiplatelet use at the time of the index event was higher in the private group (43% vs 35%, p=0.0005). Over a mean of 3.7y (SD 2.0) of follow-up, no significant differences between groups were found with regards to stroke recurrence (Adjusted HR 1.2, 95% CI 0.9-1.5), death 0.9 (0.6-1.3) or MI 1.1(0.62-1.8). CONCLUSION: Despite baseline demographic disparities between groups from public and private sites, there were no significant differences in this trial setting with regards to outcomes. It is possible that strict control of risk factors, adherence to protocol and active interventions during the trial overcame disparities associated with differences in institutions, including differences related to medical care prior to study enrolment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".