Pretreatment ASPECTS on DWI predicts 3-month outcome following rt-PA
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether the pretreatment Alberta Stroke Programme Early CT Score (ASPECTS) assessed using diffusion-weighted imaging (DWI) predicts stroke outcomes at 3 months following IV recombinant tissue-type plasminogen activator (rt-PA) therapy. METHODS: Stroke patients treated with rt-PA (0.6 mg/kg alteplase) in 10 stroke centers in Japan were retrospectively studied. ASPECTS was assessed on DWI just prior to rt-PA injection. The primary outcome was a modified Rankin Scale (mRS) score of 0-2 at 3 months. Secondary outcomes included death at 3 months and symptomatic intracerebral hemorrhage (sICH) within 36 hours. RESULTS: For the 477 patients (316 men, 71 +/- 11 years old) enrolled, the median NIH Stroke Scale score was 13 (interquartile range 7-18.5), the median ASPECTS on DWI was 8 (7-10), and sICH was identified in 15 patients (3.1%). At 3 months, 245 (51.4%) had an mRS score of 0-2, and 29 (6.1%) had died. Patients with an mRS score of 0-2 had higher median ASPECTS (9; interquartile range 8-10) than other patients (8; 6-9, p < 0.001). Using receiver operating characteristic curves, the optimal cutoff ASPECTS to predict an mRS score of 0-2 was > or =7. On multivariate regression analysis, ASPECTS > or =7 was related to an mRS score of 0-2 (odds ratio 1.85; 95% confidence interval 1.07-3.24), ASPECTS < or =4 was related to death (3.61; 1.23-9.91), and ASPECTS < or =5 was related to sICH (4.74; 1.54-13.64). CONCLUSION: ASPECTS on DWI was independently predictive of functional and vital outcomes at 3 months, as well as sICH within 36 hours, following rt-PA therapy for stroke patients.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 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".