Clinical-CT mismatch defined NIHSS ≥ 8 and CT-ASPECTS ≥ 9 as a reliable marker of candidacy for intravenous thrombolytic therapy in acute ischemic stroke
Bibliographic record
Abstract
BACKGROUND: Clinical-diffusion mismatch between stroke severity and diffusion-weighted imaging lesion volume seems to identify stroke patients with penumbra. However, urgent magnetic resonance imaging is sometimes inaccessible or contraindicated. Thus, we hypothesized that using brain computed tomography (CT) to determine a baseline "clinical-CT mismatch" may also predict the responses to thrombolytic therapy. METHODS: Brain CT lesions were measured using the Alberta Stroke Program Early CT Score (ASPECTS). A total of 104 patients were included: 79 patients with a baseline National Institutes of Health Stroke Scale (NIHSS) score ≥ 8 and a CT-ASPECTS ≥ 9 who were defined as clinical-CT mismatch-positive (P group) and 25 patients with an NIHSS score ≥ 8 and a CT-ASPECTS < 9 who were defined as clinical-CT mismatch-negative (the N group). We compared their clinical outcomes, including early neurological improvement (ENI), early neurological deterioration (END), delta NIHSS score (admission NIHSS-baseline NIHSS score), symptomatic intracranial hemorrhage (sICH), mortality, and favorable outcome at 3 months. RESULTS: Patients in the P group had a greater proportion of favorable outcome at 3 months (p = 0.032) and more frequent ENI (p = 0.038) and a greater delta NIHSS score (p = 0.001), as well as a lower proportion of END (p = 0.004) than those in the N group patients. There were no significant differences in the incidence rates of sICH and mortality between the two groups. CONCLUSIONS: Clinical-CT mismatch may be able to predict which patients would benefit from intravenous thrombolysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".