Could clinical diffusion-mismatch determined using DWI ASPECTS predict neurological improvement after thrombolysis before 3 h after acute stroke?
Bibliographic record
Abstract
BACKGROUND: Clinical-diffusion mismatch (CDM) between stroke severity and volume of diffusion-weighted imaging (DWI) lesions seems to predict penumbra. The Alberta Stroke Program Early CT Score on DWI (DWI ASPECTS) is a simple score for identifying ischaemic lesions. The authors examined whether CDM using DWI ASPECTS can predict neurological improvement in patients with acute stroke treated with intravenous tissue plasminogen activator (t-PA). METHODS: The authors enrolled consecutive patients with anterior circulation stroke treated with intravenous t-PA. The authors calculated a cut-off value for CDM using DWI ASPECTS. After excluding a group of patients with mild symptoms (National Institutes of Health Stroke Scale (NIHSS) score <8), the authors divided the patients into two groups by presence or not of CDM (a positive group (P-CDM) and a negative group (N-CDM)). The authors then compared clinical characteristics including NIHSS score and modified Rankin Scale at 90 days after intravenous t-PA. RESULTS: Seventy-one patients (male 41, mean age 74 years) were enrolled. DWI ASPECTS was linearly related to DWI lesion volume. The authors defined CDM as NIHSS scores > or =8 and DWI ASPECTS > or =7. The P-CDM group had 35 patients (61%) and the N-CDM group 22 patients (39%). NIHSS scores on admission were 15 (median) in P-CDM and 20 in N-CDM (p=0.004). NIHSS scores after intravenous t-PA improved in P-CDM but were unchanged in N-CDM (7 vs 20 at 7 days, p=0.033 on ANOVA). A favourable outcome at 90 days, defined as modified Rankin scale 0-3, was found in 46% of P-CDM patients and 14% of N-CDM patients (p=0.020). CONCLUSION: CDM determined using DWI ASPECTS may be associated with neurological improvement in patients treated with intravenous t-PA.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".