Comparison of pulsed and pseudocontinuous arterial spin‐labeling for measuring CO<sub>2</sub>‐induced cerebrovascular reactivity
Bibliographic record
Abstract
PURPOSE: To compare the performance of pulsed and pseudocontinuous arterial spin-labeling (PASL and pCASL) methods in measuring CO(2) -induced cerebrovascular reactivity (CVR). MATERIALS AND METHODS: Subjects were scanned using both ASL sequences during a controlled hypercapnia procedure and visual stimulation. CVR was computed as the percent CO(2) -induced increase in cerebral blood flow (Δ%CBF) per mmHg increase in end-tidal PCO(2) . Visually evoked responses were expressed as Δ%CBF. Resting CBF and temporal signal-to-noise ratio were also computed. Regionally averaged values for the different quantities were compared in gray matter (GM) and visual cortex (VC) using t-tests. RESULTS: Both PASL and pCASL yielded comparable respective values for resting CBF (56 ± 3 and 56 ± 4 mL/min/100g) and visually evoked responses (75 ± 5% and 81 ± 4%). Values of CVR determined using pCASL (GM 4.4 ± 0.2, VC 8 ± 1 Δ%CBF/mmHg), however, were significantly higher than those measured using PASL (GM 3.0 ± 0.6, VC 5 ± 1 Δ%CBF/mmHg) in both GM and VC. The percentage of GM voxels in which statistically significant hypercapnia responses were detected was also higher for pCASL (27 ± 5% vs. 16 ± 3% for PASL). CONCLUSION: pCASL may be less prone to underestimation of CO(2) -induced flow changes due to improved label timing control.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".