Investigation of a Novel Noninvasive Risk Analytics Algorithm With Laboratory Central Venous Oxygen Saturation Measurements in Critically Ill Pediatric Patients
Bibliographic record
Abstract
BACKGROUND: Accurate assessment of oxygen delivery relative to oxygen demand is crucial in the care of a critically ill patient. The central venous oxygen saturation (Sv o 2 ) enables an estimate of cardiac output yet obtaining these clinical data requires invasive procedures and repeated blood sampling. Interpretation remains subjective and vulnerable to error. Recognition of patient’s evolving clinical status as well as the impact of therapeutic interventions may be delayed. OBJECTIVE: The predictive analytics algorithm, inadequate delivery of oxygen (ID o 2 ) index, was developed to noninvasively estimate the probability of a patient’s Sv o 2 to fall below a preselected threshold. DERIVATION COHORT: A retrospective multicenter cohort study was conducted using data temporally independent from the design and development phase of the ID o 2 index. VALIDATION COHORT: A total of 20,424 Sv o 2 measurements from 3,018 critically ill neonates, infants, and children were retrospectively analyzed. Collected data included vital signs, ventilator data, laboratory data, and demographics. PREDICTION MODEL: The ability of the ID o 2 index to predict Sv o 2 below a preselected threshold (30%, 40%, or 50%) was evaluated for discriminatory power, range utilization, and robustness. RESULTS: Area under the receiver operating characteristic curve (AUC) was calculated for each index threshold. Datasets with greater amounts of available data had larger AUC scores. This was observed across each configuration. For the majority of thresholds, Sv o 2 values were observed to be significantly lower as the ID o 2 index increased. CONCLUSIONS: The ID o 2 index may inform decision-making in pediatric cardiac critical care settings by providing a continuous, noninvasive assessment of oxygen delivery relative to oxygen demand in a specific patient. Leveraging predictive analytics to guide timely patient care, including support for escalation or de-escalation of treatments, may improve care delivery for patients and clinicians.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".