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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".