Clinical Prediction Rules for Children: A Systematic Review
Bibliographic record
Abstract
CONTEXT: The degree to which clinical prediction rules (CPRs) for children meet published standards is unclear. OBJECTIVE: To systematically review the quality, performance, and validation of published CPRs for children, compare them with adult CPRs, and suggest pediatric-specific changes to CPR methodology. METHODS: Medline was searched from 1950 to 2011. Studies were selected if they included the development of a CPR involving children younger than 18 years. Two investigators assessed study quality, rule performance, and rule validation as methodologic standards. RESULTS: Of 7298 titles and abstracts assessed, 137 eligible studies were identified. They describe the development of 101 CPRs addressing 36 pediatric conditions. Quality standards met in fewer than half of the studies were blind assessment of predictors (47%), reproducibility of predictors (18%), blind assessment of outcomes (42%), adequate follow-up of outcomes (36%), adequate power (43%), adequate reporting of results (49%), and 95% confidence intervals reported (36%). For rule performance, 48% had a sensitivity greater than 0.95, and 43% had a negative likelihood ratio less than 0.1. For rule validation, 76% had no validation, 17% had narrow validation, 8% had broad validation, and none had impact analysis performed. Compared with CPRs for adult health conditions, quality and rule validation seem to be lower. CONCLUSIONS: Many CPRs have been derived for children, but few have been validated. Relative to adult CPRs, several quality indicators demonstrated weaknesses. Existing performance standards may prove elusive for CPRs that involve children. CPRs for children that are more assistive and less directive and include patients' values and preferences in decision-making may be helpful.
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 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 | 0.029 | 0.189 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".