THE ROLE OF ASPECTS IN PATu0130ENT SELECTION FOR ENDOVASCULAR THERAPY - CTA SOURCE IMAGES VERSUS NONCONTRAST CT
Bibliographic record
Abstract
We aimed to evaluate the association between pre-treatment Alberta Stroke Programme Early CT Score (ASPECTs) on CT angiography source images (CTA-SI) and post-treatment clinical and radiological outcomes in acute stroke patients treated with endovascular therapy (ET) and we also aimed to assess the utility of this score in patient selection for ET. The association between both scores along with final infarct and outcome were analyzed. According to the results, CTA-SI ASPECTs was better correlated with final infarct than NCCT ASPECTs. In univariate analyzes, factors associated with good outcome were age, baseline NIHSS score, and presence of diabetes mellitus. On the other hand, when an analysis differentiating patients by age was performed, the patients below 60 years of age had significantly better outcomes despite having higher baseline NIHSS scores. Finally, in multivariate analyzes, only age and baseline NIHSS score were found to be independent predictors of good outcome. Both scoring modalities were not found to be independent predictors of good outcome. Although CTA-SI ASPECTs in patient selection for ET seems to be more useful than NCCT ASPECTs, outcomes are changeable for the younger population who could continue their lives with mild or no deficits despite having a relatively low initial ASPECTs. Thus, it is reasonable to rearrange ET triage criteria and treatment targets by age groups and younger patients who are still able to benefit from treatment will not be excluded from the ET protocol.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".