89 Validation of the Severe Illness Getting Noticed Sooner (SIGNS) for Kids Tool in Children with Severe Illness
Bibliographic record
Abstract
Abstract Background Severe illness in children is associated with significant risk of permanent disability, cardiac arrest, and death. The Severe Illness Getting Noticed Sooner (SIGNS) for Kids was created by an expert panel to help parents and other caregivers identify and articulate signs of severe illness to enable timely escalation of care. SIGNS for Kids consists of 5 major items: Behaviour, Breathing, Skin, Fluids, and Response to usually effective treatments. Expert development ensured face validity, however there is no formal validity evidence to suggest the tool can identify children with severe illness. Objectives To provide preliminary validity evidence of the sensitivity of the SIGNS criteria as markers of severe illness. Design/Methods A retrospective review of paediatric deaths was performed using records from the coroner’s office. Eligible patients had an index event described and were <18 years and >36 weeks gestational age at the time of event, had observations/reports for > 6 hours prior to the index event, and were not in an intensive care unit (ICU) or under anaesthetist supervision. The index events were defined as cardiac arrest, respiratory arrest, intubation, transfer to ICU, interfacility transfer, or urgent surgical procedure. Traumatic events and infants with sudden unexplained death were excluded from analysis. The main outcome was the presence of SIGNS items. Secondary measures were the time before index events that each of the SIGNS were present, and the time to death from the index event. Analyses were descriptive. Results Two hundred records were screened. After exclusion of 145 cases with incomplete records or traumatic deaths and 5 infants with sudden unexplained death, the records of 50 children met inclusion criteria. One or more SIGNS criteria were present before index event in 48 (96%). Breathing abnormality n=26 (52%) was the most frequently observed item with a mean (SD) duration of 47 (51) hours prior to index event. Over half (n=28, 56%) had SIGNS documented for ≥24 hours before initial escalation and 42 (84%) died within 48 hours after index event. Conclusion Forty-eight of 50 children had SIGNS criteria present before index event, and half had SIGNS present >24 hours. These data provide evidence of the sensitivity of the SIGNS criteria as markers of severe illness. Future studies will focus on establishing specificity and construct validity as well as caregiver usability prior to implementation of the screening tool.
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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 | 0.020 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".