Effect of vascular radioactivity on regional values of cerebral blood flow: evaluation of methods for H(2)(15)O PET to distinguish cerebral perfusion from blood volume.
Bibliographic record
Abstract
UNLABELLED: To evaluate the appropriate model for calculating regional cerebral blood flow (rCBF) with PET and H(2)(15)O, the values obtained from 1- and 2-compartment analyses were compared. METHODS: Dynamic PET scans were performed on 12 healthy volunteers after injection of H(2)(15)O in 2 conditions of baseline and visual stimulation. Calculation of rCBF was performed using the 2-weighted integral (WI) and autoradiographic methods for the 1-compartment analysis, and the 3-WI method was followed for the 2-compartment analysis. Arterial blood radioactivity was counted continuously and corrected for delay and dispersion. The rCBF images were transformed into the Talairach space and analyzed by statistical parametric mapping to identify regional differences in the 2 methods. The values obtained from regions of interest also were compared. RESULTS: Although the difference in global CBF between the 2 models was not significant, rCBF values in the large arteries and neighbor areas were significantly greater in the 2-WI method than in the 3-WI method. However, regional differences in the activation studies were not affected when the 2 methods were compared. The images of cerebral arterial blood volume (V(0)) obtained by the 3-WI method showed a significant increase in V(0) in the visual cortex during visual stimulation. CONCLUSION: These results suggest that the rCBF values in the 1-compartment analysis were affected by radioactivity in the vessels. The 3-WI method could provide rCBF values that are less influenced by vascular radioactivity and also show differences in V(0) in PET activation studies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".