Decision tree‐based rules outperform risk scores for childhood asthma prognosis
Bibliographic record
Abstract
BACKGROUND: There are no widely accepted prognostic tools for childhood asthma; this is in part due to the multifactorial and time-dependent nature of mechanisms and risk factors that contribute to asthma development. Our study objective was to develop and evaluate the prognostic performance of conditional inference decision tree-based rules using the Pediatric Asthma Risk Score (PARS) predictors as an alternative to the existing logistic regression-based risk score for childhood asthma prediction at 7 years in a high-risk population. METHODS: The Canadian Asthma Primary Prevention Study data were used to develop, compare, and contrast the prognostic performance (area under the curve [AUC], sensitivity, and specificity) of conditional inference tree-based decision rules to the pediatric asthma risk score for the prediction of childhood asthma at 7 years. RESULTS: Conditional inference decision tree-based rules have higher prognostic performance (AUC: 0.85; 95% CI: 0.81, 0.88; sensitivity = 47%; specificity = 93%) than the pediatric asthma risk score at an optimal cutoff of ≥6 (AUC: 0.71; 95% CI: 0.67, 0.76; sensitivity = 60%; specificity = 74%). Moreover, the pediatric asthma risk score is not linearly related to asthma risk, and at any given pediatric asthma risk score value, different combinations of its pediatric asthma risk score clinical variables differentially predict asthma risk. CONCLUSION: Conditional inference tree-based decision rules could be a useful childhood asthma prognostic tool, providing an alternative way to identify unique subgroups of at-risk children, and insights into associations and effect mechanisms that are suggestive of appropriate tailored preventive interventions. However, the feasibility and effectiveness of such decision rules in clinical practice is warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".