Quality Improvement Report: Adherence to Follow-up Recommendations for Incidental Abdominal Aneurysms
Bibliographic record
Abstract
The use of national guidelines for the management of incidental radiologic findings remains low. Therefore, improving adherence to and consistency with follow-up recommendations for incidental findings was undertaken in a large academic practice. A gap analysis was performed, and incidental findings of abdominal aneurysms for which reporting management recommendations could be improved were identified. The Kotter change management framework was used, and institution-specific dictation macros were developed and implemented in February 2021 for the management of abdominal aortic aneurysms (AAAs), renal artery aneurysms (RAAs), and splenic artery aneurysms (SAAs). A retrospective medical record review was conducted for February through April in 2019, 2020, and 2021 to assess reporting adherence and imaging and clinical follow-up. Personal feedback was provided to radiologists in July 2021 with repeat data collection in September 2021. A significant increase in the number of correct follow-up recommendations was reported for incidental AAAs and SAAs after implementation of the macro (P < .001). However, there was no significant change for RAAs. Providing personal feedback to radiologists further improved adherence with standard recommendation macros for common findings and dramatically increased adherence for rare findings such as RAAs. New macros resulted in an increase in AAA and SAA imaging follow-up (P < .001). Institution-specific dictation macros were found to improve adherence to reporting recommendations for incidental abdominal aneurysms, with further improvement seen after feedback, which can have a significant effect on clinical follow-up. © RSNA, 2023
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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 | 0.168 | 0.293 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".