Correlation between Clinical-DWI Mismatch and Progressive Cerebral Infarction
Bibliographic record
Abstract
Objective To explore whether clinical-DWI mismatch(CDM)could effectively predict the occurrence of PCI and to guide early clinical diagnosis and treatment.Methods 120 patients with acute cerebral infarction in hospital no more than 24 hours were selected.According to whether cerebral infarction were progressed after admission,the patients were divided into PCI group and non-progression group.MR angiography(MRA),Diffusion Weighted Imaging(DWI) and Carotid color dopplar ultrasound were used to evaluate stenosis or occlusion of MCA,ICA and cerebral infarction position.According to the NIHSS and Alberta Stroke Program Early CT Score(ASPECTS),the patients were divided into two subgroups of CDM(+)and CDM(-)group to analyze the relationship between CDM and occurrence of PCI.Results The incidence of CDM(+)and stenosis or occlusion of MCA diagnosed by MRA in PCI group were significantly higher than that in non-progression group(P0.05).Logistic regression analysis showed that CDM,stenosis or occlusion of middle cerebral artery were independent risk predictors of PCI.Infarction site in body of lateral ventricle side or Watershed infarction has the higher incidence of PCI.Conclusion CDM,stenosis or occlusion of MCA diagnosed by MRA and cerebral infarction location could effectively predict the occurrence of PCI.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".