Development of composite clinical-radiological tool to predict functional outcomes after ischemic stroke treatment
Bibliographic record
Abstract
Quantitative radiological tools to assess acute ischemic stroke (AIS) survivor's functional outcomes are limited. While conventional qualitative scoring systems exist, they are limited by inter-rater variability due to subtle ischemic changes. Post-intervention estimation of final cerebral infarct volume (CIV) followed by assessment is a known objective radiological determinant of functional outcomes. Hence, this study aims to develop and evaluate a composite radiological tool using radiomic imaging markers to predict long-term functional risk in AIS survivors. The dataset consists of 50 AIS patients' clinical and radiological information. First, Alberta stroke programme early CT score (ASPECTS) and posterior circulation-ASPECTS regions were annotated on scans followed by mapping them with CIV. Multiple volumetric parameters were extracted, including TBV, CIV, and the proportion of CIV to TBV. These raw features were used to compute percentage volumes and perform summation and proportion analyses w.r.to ASPECTS regions. Premorbid and clinical features were also converted to meaningful representations. Principal Component Analysis and Recursive Feature Elimination (RFE) were employed to identify optimal feature sets. Finally, different combinations of composite features were utilized to train classification algorithms. Promising results were achieved with RFE using both feature combination approaches. Support vector machine (SVM) on raw features achieved the highest AUC value (0.94±0.05), while logistic regression (LR) attained AUC value (0.93±0.05). Additional analysis indicated models trained using transformed features perform more consistently and achieve superior overall performance. The study demonstrated the potential of composite tool using radiomic and clinical information to accurately predict AIS patient's risk of long-term functional outcomes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".