Feasibility of Automated Vital Sign Instability Detection in Children Admitted to the Pediatric Intensive Care Unit
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.001 |
| 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 it