Intra‐ and inter‐observer reliability using a noninvasive ultrasound cardiac output monitor in healthy anesthetized children
Bibliographic record
Abstract
BACKGROUND: Accurate and reliable evaluation of cardiac index (CI) in critically ill pediatric patients can optimize their management. Although validated, noninvasive ultrasound measurement techniques have been previously shown to be unreliable because of observer variability. OBJECTIVE: To confirm intra- and inter-observer reliability when using the noninvasive USCOM(®) in healthy anesthetized children. METHODS: Prospective observational study at the Children's Hospital of Eastern Ontario, Ottawa, included newborns to 12 years of age undergoing elective surgery or magnetic resonance imaging. The USCOM(®) was used to assess CI via aortic flow with a trans-sternal approach. Two trained observers were responsible for taking two measurements of CI each at steady state in randomized succession after stable depth of anesthesia was achieved. RESULTS: Fifty-nine patients were included. Forty-seven (80%) were between 3 and 7 years old, with 57% male. The mean difference ± sd for repeat CI measurements by each of two observers was 0.11 ± 0.47 and 0.05 ± 0.65 l·min(-1) ·m(-2) , respectively. Intra-observer reliability for these repeat measurements by each observer determined by Lin's concordance correlation coefficient was 0.92 and 0.85, respectively. The mean difference ± sd between observers was 0.16 ± 0.59 l·min(-1) ·m(-2) , and Lin's concordance correlation coefficient was 0.87. The two observers subjectively rated measurements as 'Difficult' or 'Very difficult' only 14% (16/118) and 3% (4/118) of the time, respectively. No adverse events were reported. CONCLUSION: This study confirms that the USCOM(®) is relatively easy to use and reliable in healthy children when operated by trained users.
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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 | 0.005 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".