CT Perfusion in Acute Stroke Predicts Final Infarct Volume- Inter-observer Study
Bibliographic record
Abstract
BACKGROUND: Computed tomography perfusion (CTP) is increasingly being used in the setting of acute ischemic stroke (AIS). The aim of the current study was to compare the prognostic utility of, and inter-observer variation between, baseline appearances on non-contrast CT (using Alberta Stroke Program Early CT score(ASPECTS)) and on CTP for predicting final infarct volume. We also assessed impact of training on interpretation of these images. METHODS: Retrospectively, plain head computed tomography (CT) and CTP images at presentation and CT or diffusion imaging on follow up of patients with AIS were analyzed. The lesion volume on different CTP parameters was then correlated with the final infarct volume. This analysis was done by a Neuroradiologist, a stroke Neurologist and a medical student. Kappa statistics and Intra-class correlation coefficients were used for agreement between readers. Pearson correlation coefficients were used. RESULTS: Thirty eight patients with AIS met all inclusion criteria. There was very good agreement among all readers for the CTP parameters. There was only fair agreement for ASPECT score. Correlation coefficient (r-square) between CTP parameters and final infarct volume showed that cerebral blood volume was the best parameter to predict the final infarct volume followed by cerebral blood flow and time to peak. The best reader to predict the final infarct volume on the initial CT perfusion study was the neuroradiologist followed by medical student and stroke neurologist. CONCLUSIONS: Cerebral blood volume defect correlated the best with the final infarct volume. There was a very good inter-observer agreement for all the CTP maps in predicting the final infarct volume despite the wide variation in the experience of the readers.
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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".