Normalized CT perfusion maps from 62 SAH patients
Bibliographic record
Abstract
Aneurysmal subarachnoid hemorrhage (SAH) is a severe condition that triggers numerous metabolic disruptions and complications. While the exact mechanisms implied in this cascade of events are still being investigated, monitoring cerebral perfusion seems critical to understand and prevent the occurrence of secondary deficits such as delayed cerebral ischemia (DCI). The present dataset was built within the framework of a retrospective observational study in SAH patients. Sixty-two patients were included. Clinical information at admission, aneurysm details, neurological events, and rescue treatments were collected. Of note, within this study population, 33% of patients were classified as WFNS III-V. Cerebral vasospasm (CVS) occurrence was 68%, and that of DCI was 15%. Overall, 873 CT perfusion parametric maps (TMAX, MTT, CBF) were collected and preprocessed. In particular, all data were normalized to the MNI (Montreal Neurological Institute) standardized space that allows precise within and between-subject analyses. Normalized cerebral perfusion maps can also be analyzed in specific anatomical or arterial regions of interest using the relevant segmentation templates. In this project, we developed an image processing pipeline that allowed quantitative analysis of CTP parameters' dynamics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".