Qualitative Posttreatment Diffusion-Weighted Imaging as a Predictor of 90-day Outcome in Stroke Intervention
Bibliographic record
Abstract
PURPOSE: The aim was to assess the ability of post-treatment diffusion-weighted imaging (DWI) to predict 90-day functional outcome in patients with endovascular therapy (EVT) for large vessel occlusion in acute ischemic stroke (AIS). METHODS: We examined a retrospective cohort from March 2016 to January 2018, of consecutive patients with AIS who received EVT. Planimetric DWI was obtained and infarct volume calculated. Four blinded readers were asked to predict modified Rankin Score (mRS) at 90 days post-thrombectomy. RESULTS: Fifty-one patients received endovascular treatment (mean age 65.1 years, median National Institutes of Health Stroke Scale (NIHSS) 18). Mean infarct volume was 43.7 mL. The baseline NIHSS, 24-hour NIHSS, and the DWI volume were lower for the mRS 0-2 group. Also, the thrombolysis in cerebral infarction (TICI) 2b/3 rate was higher in the mRS 0-2 group. No differences were found in terms of the occlusion level, reperfusion technique, or recombinant tissue plasminogen activator use. There was a significant association noted between average infarct volume and mRS at 90 days. On multivariable analysis, higher infarct volume was significantly associated with 90-day mRS 3-5 when adjusted to TICI scores and occlusion location (OR 1.01; CI 95% 1.001-1.03; p = 0.008). Area under curve analysis showed poor performance of DWI volume reader ability to qualitatively predict 90-day mRS. CONCLUSION: The subjective impression of DWI as a predictor of clinical outcome is poorly correlated when controlling for premorbid status and other confounders. Qualitative DWI by experienced readers both overestimated the severity of stroke for patients who achieved good recovery and underestimated the mRS for poor outcome patients. Infarct core quantitation was reliable.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".