Partnering With Patients and Families to Champion Deimplementation and Reduce Low-Value Care
Bibliographic record
Abstract
Clinical practice guidelines (CPGs) are widely used in medicine to improve quality of care.They have also been implemented as clinical decision support tools to combat medical overuse and the provision of care in the absence of a clear medical indication or when the benefit does not outweigh the risk. 1 Designing CPGs based on strong evidence can reduce practice variation, decrease harm, and maximize positive outcomes for patients.Initiatives such as the Choosing Wisely Campaign, which focus on reducing frequently overused low-value tests and treatments, often lead to CPG development, encouraging healthcare providers and organizations to make these recommendations part of routine practice.2 In this edition of Hospital Pediatrics, Cooper and colleagues describe the results of a retrospective quasiexperimental study that evaluated the impact of a CPG focused on the management of preseptal cellulitis in hospitalized children. 3 The CPG focused on antimicrobial use, blood tests and computed tomography (CT) scans, with an aim to standardize practice, reduce broad spectrum antibiotics, and reduce practice variation.Over the course of 11 years, 236 patients were included, 175 patients before and 61 patients after the CPG was implemented.After CPG implementation, there were reductions observed in all empirical antibiotic categories, including broad spectrum (100% to 66%), dual/triple therapy (47% to 16%), and methicillin-resistant Staphylococcus aureus active agents (86% to 26%).Similar changes were noticed in definitive antibiotic changes.In parallel, there were reductions in complete blood count and blood culture orders from 75% to 57% (P 5 .014)and 32% to 18% (P 5 .047),respectively.Although the decrease in broad-spectrum antibiotic use was significant, study limitations include a smaller post-CPG implementation cohort, and limited postdischarge information.Given the small sample size, the logistic regression analysis did not include important covariates such as age or sex.Another limitation is that most patients with preseptal cellulitis are discharged from the emergency department (ED) and do not require admission to hospital.Therefore, the population of patients with preseptal cellulitis included were those with more severe disease requiring hospital admission.It is unclear whether patients with milder disease also received broad spectrum antibiotics and whether this represents another opportunity for antimicrobial stewardship intervention.Interestingly, the authors noted an increase in CT scan rates after CPG implementation (pre-CPG 79% and post-CPG 84%); the reasons for this increase were not discussed.The authors noted that CT scans are "low
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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 | 0.034 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".