Description of the development and validation of the Canadian Paediatric Trigger Tool
Bibliographic record
Abstract
OBJECTIVE: To describe the process of developing and validating the Canadian Association of Paediatric Health Centres Trigger Tool (CPTT). METHODS: Five existing trigger tools were consolidated with duplicate triggers eliminated. After a risk analysis and modified Delphi process, the tool was reduced from 94 to 47 triggers. Feasibility of use was tested, reviewing 40 charts in three hospitals. For validation, charts were randomly selected across four age groups, half medical/half surgical diagnoses, from six paediatric academic health sciences centres. 591 charts were reviewed by six nurses (for triggers and adverse events (AEs)) and three physicians (for AEs only). The incidence of trigger- and AE-positive charts was documented, and the sensitivity and specificity of the tool to identify charts with AEs were determined. Identification of AEs by nurses and physicians was compared. The positive predictive value (PPV) of each trigger was calculated and the ratio of false- to true-positive AE predictors analysed for each trigger. RESULTS: Nurses rated the CPTT easy to use and identified triggers in 61.1% (361/591; 95% CI 57.2 to 65.0) of patient charts; physicians identified AEs in 15.1% (89/ 591, 95% CI 0.23 to 0.43). Over a third of patients with AEs were neonates. The sensitivity and specificity were 0.88 and 0.44, respectively. Nurse and physician AE assessments correlated poorly. The PPV for each trigger ranged from 0 to 88.3%. Triggers with a false/true-positive ratio of >0.7 were eliminated, resulting in the final 35-trigger CPTT. CONCLUSIONS: The CPTT is the first validated, comprehensive trigger tool available to detect AEs in children hospitalised in acute care facilities.
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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 | 0.085 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".