Scheduled sampling of cardiovascular parameters: how often should one collect data?
Bibliographic record
Abstract
Long‐term measurement of variables such as heart rate (HR), blood pressure (BP) and sympathetic nerve activity (SNA) via telemetry in laboratory animals has become indispensable in cardiovascular physiology. Due to the significant amount of data recorded and battery life limitations many researchers use acquisition software to intermittently collect data over short time periods at scheduled time points. The question is how much data does one need to collect to accurately reflect the underlying average value? Three separate days (0000–2400) of HR, BP and integrated renal SNA data were used from each of 6 rabbits. Each day's data (collected as 2s averages) were resampled according to 60 sampling protocols to investigate both the quantity of data collected and the number of sampling periods, e.g. 4 hours sampling per day could be two 2 hour or twenty–four 10 minute periods. The error in estimating the actual mean was calculated for each sampling protocol by comparing each day's estimated mean with that calculated using the complete data set for that day. The results show that the error in estimating the daily mean of SNA using a given protocol is approximately double that of BP and HR and good estimations (<2% error) can be achieved by scheduled sampling, particularly if short sampling periods are spread throughout the day. Supported by the Health Research Council of New Zealand and University of Auckland Research Committee.
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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 | 0.042 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".