Is diffusion imaging appearance an independent predictor of outcome after ischemic stroke?
Bibliographic record
Abstract
BACKGROUND: MR diffusion-weighted imaging (DWI) in ischemic stroke can be quantified by calculating the apparent diffusion coefficient (ADC) or measuring lesion volume. OBJECTIVE: To clarify the association between DWI lesion parameters, clinical stroke severity at baseline, and the relationship with functional outcome. METHODS: Consecutive patients with stroke were categorized for stroke type (Oxford Community Stroke Project Classification [OCSP]) and severity (Canadian Neurologic Scale [CN Scale]) before DWI. The ratio of the trace of the apparent diffusion tensor in the ischemic lesion to the mirror image area in the contralateral hemisphere was calculated ( r). The volume of the visible lesion on DWI was measured. Any visible lesion on T2-weighted imaging (T2WI) was noted. All assessments were blind to all other information. A blinded observer obtained a 6-month Rankin score. Univariate and multivariate analyses were performed to test for independent associations with outcome. RESULTS: In 108 patients, those with lower (i.e., more abnormal) r values had more severe strokes according to the CN Scale (p = 0.01) and the OCSP stroke type (p = 0.002), a large lesion on DWI (p = 0.05), a visible lesion on T2WI (p = 0.001), and poor 6-month functional outcome (p = 0.009). However, on logistic regression, neither r nor DWI lesion volume were independent predictors of 6-month outcome over and above age and stroke severity. CONCLUSION: The r is associated with functional outcome, but that is because it and DWI lesion volume are also associated with stroke severity. Although DWI lesion features are univariate surrogate outcome predictors, the authors were unable to show that they were independent outcome predictors in the current study. Differences between these and other results may be due to differences in study design, sample size, and case mix.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".