PHENOTYPING THE LEVEL OF BLOOD PRESSURE BY TELEMETRY IN MICE
Bibliographic record
Abstract
1. Using telemetry, arterial blood pressure (BP) can be measured directly over long periods in freely behaving animals without recent anaesthesia or surgery. In the present review, we discuss the strengths and limitations of this method and important considerations in using the method to characterize the BP level in mice. 2. A variety of informative statistics can be used to describe the BP level and we have made available a spreadsheet template for their calculation on a routine basis. The BP level is well summarized using the average value for an entire 24 h period or for the individual light and dark phases of the day. Such long-term averages exhibit less statistical variation than those of short recording periods. In addition, averages of the dark and light phases of the day convey information concerning circadian variations of BP. 3. The frequency distribution of BP samples provides additional information concerning the range of BP values recorded over the course of the day and can be described in terms of percentiles of the distribution that correspond with the minimum and maximum BP values and their span. 4. In mice, BP can be markedly affected by locomotor activity cycles that occur frequently throughout both the light and dark phases of the day. In addition, BP is strongly affected by ambient temperature and food intake, as well as potentially by other determinants of energy balance. Consideration of these factors may help improve accuracy and precision when phenotyping the BP level in mice.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".