Disparities in Guideline Adherence for Febrile Infants in a National Quality Improvement Project
Bibliographic record
Abstract
BACKGROUND: Interventions aimed to standardize care may impact racial and ethnic disparities. We evaluated the association of race and ethnicity with adherence to recommendations from the American Academy of Pediatrics' clinical practice guideline for febrile infants after a quality improvement (QI) intervention. METHODS: We conducted a cross-sectional study of infants aged 8 to 60 days enrolled in a QI collaborative of 99 hospitals. Data were collected across 2 periods: baseline (November 2020-October 2021) and intervention (November 2021-October 2022). We assessed guideline-concordance through adherence to project measures by infant race and ethnicity using proportion differences compared with the overall proportion. RESULTS: Our study included 16 961 infants. At baseline, there were no differences in primary measures. During the intervention period, a higher proportion of non-Hispanic white infants had appropriate inflammatory markers obtained (2% difference in proportions [95% confidence interval (CI) 0.7 to 3.3]) and documentation of follow-up from the emergency department (2.5%, 95% CI 0.3 to 4.8). A lower proportion of non-Hispanic Black infants (-12.5%, 95% CI -23.1 to -1.9) and Hispanic/Latino infants (-6.9%, 95% CI -13.8 to -0.03) had documented shared decision-making for obtaining cerebrospinal fluid. A lower proportion of Hispanic/Latino infants had appropriate inflammatory markers obtained (-2.3%, 95% CI -4.0 to -0.6) and appropriate follow-up from the emergency department (-3.6%, 95% CI -6.4 to -0.8). CONCLUSIONS: After an intervention designed to standardize care, disparities in quality metrics emerged. Future guideline implementation should integrate best practices for equity-focused QI to ensure equitable delivery of evidence-based care.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".