Which pressure to believe? A comparison of direct arterial with indirect blood pressure measurement techniques in the pediatric intensive care unit
Bibliographic record
Abstract
OBJECTIVE: To determine the accuracy of arterial blood pressure monitoring using 1) direct arterial; 2) automated oscillometric; and 3) sphygmomanometer/Doppler ultrasound measurements in pediatric intensive care patients comparing methods 1) and 2) with 3), the gold standard used to define normal blood pressure. DESIGN: Prospective observational study. SETTING: Pediatric intensive care unit of a tertiary care pediatric teaching hospital. PATIENTS: Forty children (birth to 17 yrs) admitted to the pediatric intensive care unit with various clinical conditions requiring a radial arterial catheter for continuous arterial blood pressure monitoring. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Each subject had measurements taken every 6 hrs over a 24-hr period. Each set of measurements were: direct arterial blood pressure, indirect blood pressure using the Phillips automated oscillometric device, and indirect blood pressure using the sphygmomanometer and Doppler ultrasound. Analysis used the Bland-Altman plot followed by paired t testing to compare the three different methods. One hundred sixty triads of measurements were analyzed. There were no significant differences between the methods of blood pressure measurement when groups were analyzed based on age. When analyzed by age-specific normo-, hypo-, and hypertensive criteria, arterial blood pressure measurements agree closely with Doppler ultrasound readings, whereas systolic arterial blood pressure measurements were lower than indirect blood pressure using the Phillips automated oscillometric device readings in the hypotensive group (p < .001). In the hypertensive group, the systolic arterial blood pressure values were higher and indirect blood pressure using the Phillips automated oscillometric device readings lower (p < .001) than Doppler ultrasound (p = .03). There was no clinically significant difference between methods in the normotensive group. Diastolic blood pressure measurements were higher by arterial blood pressure in normotensive and hypertensive groups but no different in the hypotensive group. CONCLUSION: Outside the normotensive range, the automated readings were higher during hypotension and lower during hypertension compared with the arterial and Doppler ultrasound methods. The arterial blood pressure was closer to the gold standard Doppler ultrasound blood pressure in all three blood pressure groups.
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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 | 0.008 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".