To regulate, or not to regulate? The devious history of cerebral blood flow control
Bibliographic record
Abstract
'Science is simply common sense at its best, that is, rigidly accurate in observation and merciless to fallacy in logic' Science is accurate in observation, only if the method of observation is valid. The classic cerebral autoregulation (CA) curve first described by Lassen (1959) was falsely interpreted, and suggested that the brain is capable of maintaining a constant perfusion pressure throughout a wide range of mean arterial pressure (e.g. 50–150 mmHg). The assumption that the human brain has such extensive regulatory capabilities resulted in several unfortunate medical events where individuals undergoing general anaesthesia in the upright body position suffered from ischaemic brain injury due to inadequate pharmacological induced cerebral perfusion pressure (Pohl & Cullen, 2005). More consistent with the early observations of Bayliss & Hill (1895), it is now known that the CA range of the human brain is unremarkable; even modest static and dynamic changes in blood pressure can modulate changes in cerebral perfusion. These reported cerebral ischaemic events highlight the clinical importance of a better understanding of CA. 'The great tragedy of science – the slaying of a beautiful hypothesis by ugly fact.' Similar to the appealing CA curve suggested by Lassen (1959), the landmark investigation (cited >700 times) by Tiecks et al. (1995) has also been subject to recent scrutiny due to: (1) the use of transcranial Doppler ultrasound (TCD) to measure brain blood flow, (2) the assumption that different measurements of dCA correlate between each other, and (3) the influence of anaesthesia. First, the utility of TCD has been challenged in recent years, although this was largely unknown by Tiecks and colleagues when the investigation was conducted. Typically, TCD is employed under the assumption that the diameter of the insonated cerebral vessel remains unchanged, but this assumption does not hold true under a variety of physiological stimuli. Second, the method of assessing dCA is more important than once thought, as it has become apparent that all methods of determining dCA (e.g. bilateral thigh cuff, transfourier analysis, Oxford technique), do not correlate with one another. Third, under anaesthesia, cerebral metabolic rate is markedly reduced, making it difficult to compare the results from Tiecks et al. (1995) to other studies investigating the relationship between sCA and dCA during wakefulness (e.g. de Jong et al. 2017). 'Try to learn something about everything and everything about something.' None declared. M.M.T. is funded by a Natural Sciences and Engineering Research Council of Canada doctoral grant. P.N.A. is funded by a Canada Research Chair grant.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.035 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".