Education About Crying in Normal Infants Is Associated with a Reduction in Pediatric Emergency Room Visits for Crying Complaints
Bibliographic record
Abstract
OBJECTIVE: The primary aim of this study was to determine whether there was any change in visits of 0- to 5-month old infants to the medical emergency room (MER) of a metropolitan pediatric hospital after province-wide implementation of a public health prevention program that teaches new parents about the properties of early crying in normal infants. METHODS: Free-text descriptions of Presenting Complaint and Final Diagnosis on electronic MER clinic visit files were used to classify infants as cases of infant crying not due to disease. Annual crying case visits as a percent of MER visits were analyzed pre- and post-introduction of the prevention program. RESULTS: Before the program, crying case visits represented 724 of 20,394 MER visits (3.5%). The age-specific pattern of MER visits for crying peaked at 6 weeks and was similar to the previously reported age-specific pattern of amounts of crying in the community. After program implementation, crying cases were reduced by 29.5% (p < .001). The most significant reductions were for crying visits in the first to third months of life. CONCLUSION: The findings imply that improved parental knowledge of the characteristics of normal crying secondary to a public health program may reduce MER use for crying complaints in the early months of life.
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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 | 0.001 | 0.007 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".