Characterization of volumetric flow rate waveforms in the normal internal carotid and vertebral arteries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Knowledge of normal cerebrovascular volumetric flow rate (VFR) dynamics is of interest for establishing baselines, and for providing input data to cerebrovascular model studies. Retrospectively gated phase contrast magnetic resonance imaging was used to measure time-resolved VFR waveforms from the two internal carotid arteries (ICA) and two vertebral arteries (VA) of 17 young, normal volunteers (16M:1F) at rest in a supine posture. After normalizing each waveform to its respective cycle-averaged VFR, the timing and amplitude of feature points from the individual waveforms were averaged together to produce archetypal ICA and VA waveform shapes. Despite significant inter-individual differences in cycle-averaged VFR within the ICA compared to VA (275+/-52 versus 91+/-18 mL min-1), the respective waveform shapes were qualitatively similar overall. The VA waveform shape did, however, exhibit significantly higher amplitudes (e.g., peak:average VFR of 1.78+/-0.30 versus 1.66+/-0.16; p<0.05) and significantly higher variability both between and within subjects. A significant correlation was observed between peak and cycle-averaged VFR, suggesting that the representative waveform shapes presented here-when scaled by an individual's cycle-averaged VFR-may be used to characterize normal ICA and VA flow rate dynamics. This capability may be of particular utility for studies where cerebrovascular flow dynamics are required, but only average flow rates are available.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it