Reliability of non-invasive blood pressure monitoring in sick very low birth weight preterm infants
Bibliographic record
Abstract
Background Accurate blood pressure (BP) measurement is crucial for assessing hemodynamic features in infants with very low birth weight (VLBW). Various methods are available to monitor BP, including both invasive arterial blood pressure (IBP) and non-invasive blood pressure (NIBP). IBP monitoring, although accurate, poses risks due to its invasive approach, while NIBP monitoring, despite being safer, may lack precision. Objective To evaluate the agreement between IBP and NIBP measurements and assess the reliability of NIBP in diagnosing hypotension among VLBW infants. Methods This study was conducted in the neonatal intensive care unit (NICU) of King Fahd Armed Forces Hospital, Jeddah, Saudi Arabia. VLBW infants (n = 40) with IBP measured via an umbilical arterial catheter (UAC) or peripheral arterial line (PAL) were included. Simultaneous NIBP measurements were taken using appropriately sized cuffs. Results Across 3260 paired measurements, NIBP showed a mean bias of −6 ± 10 mmHg compared to IBP, with 95% limits of agreement from −25.6 to 13.6 mmHg. NIBP overestimated systolic, diastolic, and mean arterial pressures (MAP) by 8.9 mmHg, 7.47 mmHg, and 6.76 mmHg, respectively (all p < 0.001). Overall, 58% of pairs exceeded ±15% of invasive MAP. Invasive hypotension prevalence was 4.3% (MAP < gestational age) and 21.7% (MAP <30 mmHg). Receiver operating characteristics analysis showed modest diagnostic accuracy of NIBP (area under the curve = 0.627 and 0.707), with specificity >90% but sensitivity only 26.6%. Conclusions NIBP overestimates blood pressure, and IBP is the gold standard method for accurate BP assessment in VLBW infants.
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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 | 0.006 | 0.026 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".