Improving Guideline-Concordant Care for Febrile Infants Through a Quality Improvement Initiative
Bibliographic record
Abstract
OBJECTIVES: We aimed to examine the impact of a quality improvement (QI) collaborative on adherence to specific recommendations within the American Academy of Pediatrics' Clinical Practice Guideline (CPG) for well-appearing febrile infants aged 8 to 60 days. METHODS: Concurrent with CPG release in August 2021, we initiated a QI collaborative involving 103 general and children's hospitals across the United States and Canada. We developed a multifaceted intervention bundle to improve adherence to CPG recommendations for 4 primary measures and 4 secondary measures, while tracking 5 balancing measures. Primary measures focused on guideline recommendations where deimplementation strategies were indicated. We analyzed data using statistical process control (SPC) with baseline and project enrollment from November 2020 to October 2021 and the intervention from November 2021 to October 2022. RESULTS: Within the final analysis, there were 17 708 infants included. SPC demonstrated improvement across primary and secondary measures. Specifically, the primary measures of appropriately not obtaining cerebrospinal fluid in qualifying infants and appropriately not administering antibiotics had the highest adherence at the end of the collaborative (92.4% and 90.0% respectively). Secondary measures on parent engagement for emergency department discharge of infants 22 to 28 days and oral antibiotics for infants 29 to 60 days with positive urinalyses demonstrated the greatest changes with collaborative-wide improvements of 16.0% and 20.4% respectively. Balancing measures showed no change in missed invasive bacterial infections. CONCLUSIONS: A QI collaborative with a multifaceted intervention bundle was associated with improvements in adherence to several recommendations from the AAP CPG for febrile infants.
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 | 0.036 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".