Blood pressure agreement between ideal and loose-fitting cuffs in anesthetized dogs
Bibliographic record
Abstract
IMPORTANCE: Indirect blood pressure monitoring is used frequently in veterinary medicine. Blood pressure cuff looseness has not been investigated as a cause of erroneous measurements. OBJECTIVE: To determine if cuff looseness affects blood pressure measurements in healthy anesthetized dogs. METHODS: Between December 2020 and May 2022 at an institutional practice, 62 anesthetized healthy dogs were separated into two groups: ≤ 20 kg and > 20 kg. Tail base circumference of each dog was measured, and baseline was defined as ideal (0% looseness factor). The cuff was manually loosened sequentially from 0% to 2%, 5%, 8%, 10% and 15% looseness factors. High definition oscillometry was used to measure systolic (SAP), mean (MAP) and diastolic (DAP) arterial blood pressures. Bland and Altman for repeated measures was used to analyze SAP, MAP and DAP measurements of baseline and each looseness factor. Acceptable bias and limits of agreement (LoA) were set using American College of Veterinary Internal Medicine guidelines. RESULTS: All biases were acceptable. In dogs ≤ 20 kg, LoA for all SAP looseness factors and MAP looseness factors of 10% and 15% did not fall within the guidelines. In dogs > 20 kg, LoA for all measurements except SAP 5%, 8% and 10% looseness factors fell within the guidelines. CONCLUSIONS AND RELEVANCE: Loosening a cuff up to 15% did not result in significant changes to blood pressure measurements of healthy anesthetized dogs using high definition oscillometry.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".