Feasibility of Automated Vital Sign Instability Detection in Children Admitted to the Pediatric Intensive Care Unit
Bibliographic record
Abstract
Children admitted to a Pediatric Intensive Care Unit (PICU) are at risk of deterioration, which can lead to a cardiac arrest if undetected. Outcomes after pediatric cardiac arrest remain poor, even for witnessed, inhospital events. Thus, early detection of deterioration is paramount; ideally long before the risk of harm increases. Vital signs trends of patients admitted to the PICU at BC Children’s Hospital were extracted from local outcomes registries (n=96). A rule-based algorithm (RBA) for detecting vital signs instabilities was developed; we did so in the expectation of enhancing clinician trust compared to black box approaches such as deep neural networks. Two PICU physicians provided expert classifications for episodes indicative of vital signs instability or their absence. The RBA’s best result generated 91.6% correct, 6% false negatives, and 3% false positives on the test data (n=29) showing promise for eventual application in a clinical setting. Future research is needed to refine the algorithm and implement it in clinical practice.
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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 | 0.003 | 0.022 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".