The predictive value of a targeted posterior fossa multimodal stroke protocol for the diagnosis of acute posterior ischemic stroke
Bibliographic record
Abstract
There is limited but growing research regarding the accuracy of CTP in diagnosing acute posterior ischemia stroke. We sought to evaluate the diagnostic accuracy of an incremental multimodal CT protocol in acute posterior ischemic stroke. Retrospective review of incremental NCCT, CTA-source images and CTP use in 82 consecutive patients with acute posterior ischemic stroke. Readers were blinded to infarct status on follow-up imaging (MRI or CT). Predictive effects of observed diagnostic accuracy and confidence score were quantified with the entropy r 2 value. Sensitivity, specificity, and CI were calculated accounting for multiple reader assessments. Receiver Operating Characteristic analyses, including Area Under the Curve, were conducted for the three modalities. Inter-reader agreement was established with Intraclass Correlation Coefficient. Follow-up imaging confirmed infarct in 69/82 (84 %) patients. Multimodal protocol with CTP, outperforms CTA-source images and NCCT for correct acute posterior ischemia stroke diagnosis. The Area Under the Curve was 0.741 (95 % CI 0.708–0.773); 0.70 (95 % CI 0.663–0.731, P = 0.03) and 0.62 (95 % CI 0.588–0.659, P < 0.0001), respectively. Incrementally improved correlation between observed and actual diagnosis (r 2 = 0.09, 0.26 and 0.32) and a higher rate of certainty (51.4, 69.3 and 81.7 %) was demonstrated for NCCT, CTA-source images and CTP respectively. Inter-reader agreement for the actual diagnosis was good and improved from 0.68 to 0.83 with incremental multimodal CT use. CTP enhances confident and correct infarct diagnosis over NCCT and CTA-source images in acute posterior ischemia stroke.
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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 | 0.008 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".