On the use and misuse of cerebral hemodynamics terminology using transcranial Doppler ultrasound: a call for standardization
Bibliographic record
Abstract
Cerebral hemodynamics, e.g., cerebral blood flow, can be measured and quantified using many different methods, with transcranial Doppler ultrasound (TCD) being one of the most commonly used approaches. In human physiology, the terminology used to describe metrics of cerebral hemodynamics are inconsistent and in some instances technically inaccurate; this is especially true when evaluating, reporting, and interpreting measures from TCD. Therefore, this perspective article presents recommended terminology when reporting cerebral hemodynamic data. We discuss the current use and misuse of the terminology in the context of using TCD to measure and quantify cerebral hemodynamics and present our rationale and consensus on the terminology that we recommend moving forward. For example, one recommendation is to discontinue the use of the term "cerebral blood flow velocity" in favor of "cerebral blood velocity" with precise indication of the vessel of interest. We also recommend clarity when differentiating between discrete cerebrovascular regulatory mechanisms, namely, cerebral autoregulation, neurovascular coupling, and cerebrovascular reactivity. This will be a useful guide for investigators in the field of cerebral hemodynamics research.
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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 | 0.034 | 0.099 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.016 | 0.037 |
| Insufficient payload (model declined to judge) | 0.003 | 0.006 |
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".