Systematic review of interventions that improve provider compliance to imaging guidelines for prostate cancer
Bibliographic record
Abstract
INTRODUCTION: Radiographic staging with bone scan or computed tomography is not indicated for men with low-risk prostate cancer. Physician compliance with these imaging recommendations has been widely variable, leading to inappropriate testing and increased costs. The purpose of this systematic review was to identify and learn from interventions associated with improved physician compliance to imaging guidelines for prostate cancer staging. METHODS: This systematic review followed Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. PubMed was searched through January 2022 for the following medical subject headings (MeSH) terms: ('practice patterns, physicians' or 'guideline adherence' or 'unnecessary procedures' or 'quality improvement') and ('prostatic neoplasms/diagnostic imaging'). Inclusion required discussion of an intervention for physician compliance to prostate cancer imaging guidelines and specific data describing associated outcomes. Publications focused on other malignancies or without this intervention, evaluation, or data were excluded. RESULTS: Of 82 papers screened, only five met inclusion criteria - representing 12 802 patients. Each focused on reducing unnecessary imaging and demonstrated statistically significant post-intervention improvement of physician compliance to imaging guidelines for staging prostate cancer. Four were multidimensional, with education, clinical champions, and performance feedback. One used the unidimensional intervention of an electronic medical record (EMR)-based clinical reminder order check (CROC). No studies used randomization or a control group. CONCLUSIONS: Post-intervention improvement in physician compliance to imaging guidelines for staging prostate cancer has been associated with EMR-based CROC and combination interventions using clinical champions, education, and feedback. This has been observed at individual institutions and larger organizations spanning a region or state.
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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 | 0.010 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".