Rourke Baby Record 2017: Clinical update for preventive care of children up to 5 years of age.
Bibliographic record
Abstract
OBJECTIVE: To describe the process and evidence used to update preventive care recommendations in the 2017 Rourke Baby Record to assist primary care providers' decisions around which maneuvers to prioritize and implement in practice. QUALITY OF EVIDENCE: A search of the literature from June 2013 to June 2016 was conducted, using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) methodology to critically appraise primary research studies, and recommendations were changed where there was substantial support from the new literature. MAIN MESSAGE: The important changes in preventive care recommendations for children up to 5 years of age include the addition of body mass index monitoring as of 2 years of age; stronger evidence to support the introduction of allergenic foods without delay (strength of recommendation change from fair to good); the recommendation to ask validated questions regarding the effects of poverty; evidence showing no safe level of lead exposure in children; the recommendation of a daily sleep duration; the upgrade of recommendation strength from fair to good of items related to the prevention and detection of adverse childhood experiences, including assessment of bruising in babies younger than 9 months; and blood pressure monitoring only for children at risk. CONCLUSION: Early childhood exposures and habits have short- and long-term health consequences. The Rourke Baby Record will continue to publish updates to ensure that primary care providers are equipped to promote lifelong health and well-being through evidence-informed care in young children.
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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 | 0.015 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
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".