Impact of a National Guideline on Antibiotic Selection for Hospitalized Pneumonia
Bibliographic record
Abstract
BACKGROUND: We evaluated the impact of the 2011 Pediatric Infectious Diseases Society/Infectious Diseases Society of America pneumonia guideline and hospital-level implementation efforts on antibiotic prescribing for children hospitalized with pneumonia. METHODS: We assessed inpatient antibiotic prescribing for pneumonia at 28 children’s hospitals between August 2009 and March 2015. Each hospital was also surveyed regarding local implementation efforts targeting antibiotic prescribing and organizational readiness to adopt guideline recommendations. To estimate guideline impact, we used segmented linear regression to compare the proportion of children receiving penicillins in March 2015 with the expected proportion at this same time point had the guideline not been published based on a projection of a preguideline trend. A similar approach was used to estimate the short-term (6-month) impact of local implementation efforts. The correlations between organizational readiness and the impact of the guideline were estimated by using Pearson’s correlation coefficient. RESULTS: Before guideline publication, penicillin prescribing was rare (<10%). After publication, an absolute increase in penicillin use was observed (27.6% [95% confidence interval: 23.7%–31.5%]) by March 2015. Among hospitals with local implementation efforts (n = 20, 71%), the median increase was 29.5% (interquartile range: 19.6%–39.1%) compared with 20.1% (interquartile rage: 9.5%–44.5%) among hospitals without such activities (P = .51). The independent, short-term impact of local implementation efforts was similar in magnitude to that of the national guideline. Organizational readiness was not correlated with prescribing changes. CONCLUSIONS: The publication of the Pediatric Infectious Diseases Society/Infectious Diseases Society of America guideline was associated with sustained increases in the use of penicillins for children hospitalized with pneumonia. Local implementation efforts may have enhanced guideline adoption and appeared more relevant than hospitals’ organizational readiness to change.
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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 | 0.024 | 0.158 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".