The Stroke Prognosis Instrument II (SPI-II)
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: In 1991 we developed the Stroke Prognosis Instrument (SPI-I) to stratify patients with transient ischemic attack or ischemic stroke by prognosis for stroke or death in 2 years. In this article we validate and improve SPI-I (creating SPI-II). METHODS: To validate SPI-I, we applied it to 4 test cohorts and calculated pooled outcome rates. To create SPI-II, we incorporated new predictive variables identified in 1 of the test cohorts and validated it in the other 3 cohorts. RESULTS: For SPI-I, pooled rates (all 4 test cohorts) of stroke or death within 2 years in risk groups I, II, and III were 9%, 17%, and 24%, respectively (P<0.01, log-rank test). SPI-II was created by adding congestive heart failure and prior stroke to SPI-I. Each patient's risk group was determined by the total score for 7 factors: congestive heart failure (3 points); diabetes (3 points); prior stroke (3 points); age >70 years (2 points); stroke for the index event (not transient ischemic attack) (2 points); hypertension (1 point); and coronary artery disease (1 point). Risk groups I, II, and III comprised patients with 0 to 3, 4 to 7, and 8 to 15 points, respectively. For SPI-I, pooled rates (3 cohorts excluding the SPI-II development cohort) of stroke or death within 2 years in risk groups I, II, and III were 9%, 17%, and 23%, respectively. For SPI-II, pooled rates were 10%, 19%, and 31%, respectively. In receiver operator characteristic analysis, the area under the curve was 0.59 (95% CI, 0.57 to 0.60) for SPI-I and 0.63 (95% CI, 0.62 to 0.65) for SPI-II, confirming the better performance of the latter. CONCLUSIONS: Compared with SPI-I, SPI-II achieves greater discrimination in outcome rates among risk groups. SPI-II is ready for use in research design and may have a role in patient counseling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".