Validation of finger blood pressure monitoring in children
Bibliographic record
Abstract
BACKGROUND: Continuous beat-to-beat blood pressure monitoring permits the rapid detection of blood pressure fluctuations for cardiovascular reflex testing and clinical haemodynamic monitoring. In adults, this can be achieved noninvasively with high accuracy, using finger blood pressure monitoring with volume clamp photoplethysmography. However, data are lacking on the validity of finger blood pressure monitoring in children compared to the gold standard - invasive intra-arterial blood pressure monitoring. AIM: We aimed to evaluate the accuracy of novel noninvasive index and middle finger arterial pressure (FinAP) measurements in children. METHODS: Using prototype paediatric finger cuffs, we compared: mean differences, bias and limits of agreement (Bland-Altman analyses); cumulative percentage differences [clinical grade A-D (based on the percentage of heartbeats in agreement with the standard)]; and waveform morphology (regression analysis and smoothing) between both raw FinAP (Finapres NOVA) and reconstructed finger-brachial arterial pressure (reBAP) compared to intra-arterial blood pressure measurements. RESULTS: Eighteen children were tested (aged 3-13 years; 12 male), with data from 13 included in the analysis. The bias for reBAP for the middle finger was 1.8±6.9, 0.3±6.1 and 0.4±5.3 mmHg for systolic, diastolic and mean arterial pressure, with clinical grades of C, B and A, respectively. reBAP improved numerical accuracy, but reduced waveform morphological agreement. CONCLUSION: Middle finger arterial measurements with waveform reconstruction provide an acceptable surrogate for invasive intra-arterial recording in children. Finger blood pressure monitoring is a novel comfortable, convenient and accurate alternative approach for noninvasive beat-to-beat blood pressure monitoring in children.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".