Abstract TP207: Baseline Characteristics And Functional Outcomes Of Pontine And Non-pontine Infarcts: On Behalf Of The Secondary Prevention Of Small Subcortical Strokes (SPS3) Investigators
Bibliographic record
Abstract
BACKGROUND: Pontine stroke accounts for 7% of ischemic infarcts and 25% are due to cerebral small vessel disease (CVSD). Risk factors and functional outcomes distinguishing pontine from non-pontine small vessel infarcts are not well-defined. METHODS: Data are from the Secondary Prevention of Small Subcortical Strokes (SPS3) trial. Patients have MRI-proven small vessel infarcts. The analysis compared baseline characteristics, clinical features and functional outcomes of participants with pontine and non-pontine infarcts. RESULTS: Of 2871 participants, 634 (22%) had pontine infarcts. Pontine patients were more often male (69% vs 61%, p=0.0009), with history of hypertension (82% vs 73%, p<0.0001) and diabetes (45% vs 34%, p<0.0001). More Hispanics (38% vs 28%), Blacks (20% vs 15%), and fewer Whites (40% vs 54%, p<0.0001) had pontine infarcts. Pontine participants were more likely to have no white matter abnormalities (WMA) on MRI (13% vs 2%, p<0.0001). There was no difference in mean age (64 vs 63), presence of multiple infarcts (39% vs 40%), or rates of intra- (18% vs 16%) or extracranial (2% vs 3%) stenosis. Pontine infarcts had worse functional outcomes (mRS≤2 29% vs 23%, p<0.0001) and higher rates of MI on followup (1.1%/yr vs 0.5%, HR 2.2(1.3-3.7)). There was no significant difference for rates of stroke (2.5%/yr vs 2.6%) or all-cause mortality (2.2%/yr vs 1.6%) on followup. In a multivariable logistic regression model, significant differences persisted for gender (OR 1.4(1.1-1.8)), history of hypertension (1.6(1.2-2.1)) and diabetes (1.4(1.1-1.8)), white vs. black race (0.5(0.4-0.7)), and degree of WMA (moderate vs mild 0.6(0.5-0.9); severe vs mild 0.5(0.4-0.7)). CONCLUSIONS: Participants with pontine infarcts had distinct baseline characteristics from those with non-pontine infarcts. These differences suggest that a stroke mechanism distinct from conventional CVSD may be responsible for a majority of pontine infarcts and may help to target future therapeutic strategies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".