Reliability of Measuring Lesion Volumes in Transient Ischemic Attack and Minor Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Lesion volume measurements in disabling ischemic stroke have excellent reliability, but it is not clear whether this is also true for small lesions. We assessed the reliability of measuring baseline and follow-up lesion volumes in transient ischemic attack and minor stroke. METHODS: Patients who presented with a transient ischemic attack or minor stroke (NIHSS < or = 3) who had brain MRI within 24 hours from symptom onset and at 30-day follow-up and had an acute lesion on baseline MRI were included. Using semiautomated software, 4 stroke fellows independently assessed ischemic lesions twice on acute diffusion-weighted imaging and follow-up fluid-attenuated inversion recovery. RESULTS: Eighty patients were included, with a median baseline NIHSS of 1. Mean baseline diffusion-weighted imaging lesion volume was 3.4+/-7.4 mL (87.5% had <5 mL). There was excellent inter-rater/intrarater reliability, with intraclass correlation coefficients of 0.94/0.96 for acute diffusion-weighted imaging, 0.74/0.92 for follow-up fluid-attenuated inversion recovery, and 0.81/0.93 for growth. CONCLUSIONS: We found excellent concordance between and within raters for acute diffusion-weighted imaging and 30-day follow-up fluid-attenuated inversion recovery lesion volume measurements in patients with transient ischemic attack and minor stroke.
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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 | 0.010 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".