Non-invasive cardiac output monitoring with electrical velocimetry after cardiac surgery in infants
Bibliographic record
Abstract
INTRODUCTION: Low cardiac output following cardiac surgery is a major determinant of outcome that may be improved by early detection, yet there are no widely accepted methods for its measurement in young children. We evaluated the feasibility of the routine use of electrical velocimetry, a non-invasive technique providing continuous measurement of cardiac output, in infants in the early postoperative period. METHODS: With ethical approval and parental consent, infants undergoing cardiac surgery were recruited. The ICON electrical velocimetry monitor was attached on admission to the intensive care unit (ICU) and remained for up to 24h. RESULTS: =0.02) and no correlation with blood pressure, central venous pressure or arteriovenous oxygen difference. Data were recorded for a median of 19h (range 5-24), with lead detachment or movement artefact the most common causes of data loss. There was marked minute-to-minute variability, with 25% of consecutive measurements having >5% variability. CONCLUSION: Cardiac index measured by electrical velocimetry in infants in the early postoperative period is impaired by frequent data loss and marked intrapatient variability. Our feasibility study suggests that it is unsuitable for use as a routine monitoring tool in the setting of postsurgical ICU care.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".