Measuring level of agreement between values obtained by directly measured blood pressure and ultrasonic Doppler flow detector in cats
Bibliographic record
Abstract
OBJECTIVE: To determine if blood pressure measured with an ultrasonic Doppler flow detector (Doppler) is in good agreement with directly measured blood pressures in anesthetized cats. DESIGN: Prospective observational study. SETTING: University veterinary teaching hospital. ANIMALS: Thirty-nine cats undergoing routine neutering. INTERVENTIONS: Cats were divided into 2 groups; 19 cats enrolled in Group A had a 24-Ga catheter inserted into a dorsal pedal artery; 20 cats in Group B had a 20-Ga catheter placed in a femoral artery. In both groups, systolic, diastolic, and mean arterial pressures were directly measured using a validated pressure measurement system. Indirect values were compared against direct blood pressure measurements. RESULTS: There was no difference between groups. Overall, there was poor agreement with a significant bias observed between Doppler and directly measured blood pressures. For the systolic arterial pressure the bias was -8.8 with limits of agreements (LOA) of -39.3 and 21.7. For the mean arterial pressure, the bias was 14.0 with LOA of -13.9 and 41.9. For the diastolic arterial pressure, the bias was 27.9 with LOA of -4.4 and 60.2. Methodology, weight, sex, and replicates did not have a significant effect on the difference between indirect and direct measurements in any model. CONCLUSIONS: Results suggest poor agreement between Doppler values and directly measured blood pressures in anesthetized cats. Use of Doppler in cats could be misleading and readings should be interpreted with caution in a clinical context.
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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 | 0.015 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".