Phase contrast MR imaging measurements of blood flow in healthy human cerebral vessel segments
Bibliographic record
Abstract
Phase contrast (PC) magnetic resonance imaging was used to obtain velocity measurements in 30 healthy subjects to provide an assessment of hemodynamic parameters in cerebral vessels. We expect a lower coefficient-of-variation (COV) of the volume flow rate (VFR) compared to peak velocity (vpeak) measurements and the COV to increase in smaller caliber arteries compared to large arteries.PC velocity maps were processed to calculate vpeak and VFR in 26 vessel segments. The mean, standard deviation and COV, of vpeak and VFR in each segment were calculated. A bootstrap-style analysis was used to determine the minimum number of subjects required to accurately represent the population. Significance of vpeak and VFR asymmetry was assessed in 10 vessel pairs.The bootstrap analysis suggested that averaging more than 20 subjects would give consistent results. When averaged over the subjects, vpeak and VFR ranged from 5.2 ± 7.1 cm s(-1), 0.41 ± 0.58 ml s(-1) (in the anterior communicating artery; mean ± standard deviation) to 73 ± 23 cm s(-1), 7.6 ± 1.7 ml s(-1) (in the left internal carotid artery), respectively. A tendency for VFR to be higher in the left hemisphere was observed in 88.8% of artery pairs, while the VFR in the right transverse sinus was larger. The VFR COV was larger than vpeak COV in 57.7% of segments, while smaller vessels had higher COV.Significance and potential impact: VFR COV was not generally higher than vpeak COV. COV was higher in smaller vessels as expected. These summarized values provide a base against which vpeak and VFR in various disease states can be compared.
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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 | 0.002 |
| 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".