Supplementary Material for: Proof of Concept Study: Relating Infarct Location to Stroke Disability in the NINDS rt-PA Trial
Bibliographic record
Abstract
Background: The summed Alberta Stroke Program Early CT Score (ASPECTS) for noncontrast head CT scan represents the extent of early brain ischemia and has been shown to be useful for predicting stroke outcome. The ASPECTS template contains information on anatomical location which so far has not been used in analysis. This may not have been done because adjacent brain regions have related functions and share vascular territory. The task of relating neurological deficit to infarct localization requires brain imaging analysis tools which deal with this issue of relatedness or collinearity. We have previously used partial least squares with penalized logistic regression (PLR) to handle this problem of collinearity. A disadvantage of this method is that it cannot be performed at the bedside and requires processing and analysis in the imaging laboratory. PLR is a simpler analytic tool compared to partial least squares with PLR for dealing with this issue of relatedness (collinearity). It provides results in terms of β coefficients related to specific infarct locations in a manner that is intuitively understood by clinicians. In this exploratory analysis, we hypothesized that infarct location as represented by the individual ASPECTS region may be independently related to disability. Methods: ASPECTS from CT scans of patients in the National Institute of Neurological Disorders and Stroke (NINDS) recombinant tissue plasminogen activator (rt-PA) Study were obtained. Due to the collinearity between the ASPECTS regions, we used PLR to determine the independent associations of exposures (rt-PA), demographic variables (age and sex), and imaging (ASPECTS location) with poor outcome as defined by a modified Rankin Scale score of >2. Results: In 607/624 subjects with ASPECTS readings, variables significantly associated with poor outcome included: interactions between ASPECTS M6 region (primary motor cortex/parietal lobe) and age (p = 0.004), lentiform nucleus and age (p = 0.007), and blood sugar level and age (p = 0.01). The model suggested that older age or involvement of either M6 or lentiform nucleus slightly increased the odds of disability. However, the predominant effect was driven by rt-PA which reduced the odds of poor disability (OR 0.597, 95% CI 0.425-0.838, p = 0.003). This may potentially explain why certain patients have smaller gains from rt-PA treatments. Conclusion: At an older age, specific infarct locations may be associated with a poorer outcome in this exploratory re-analysis of the NINDS rt-PA Study.
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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 | 0.014 | 0.091 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.804 | 0.190 |
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".