Appropriateness of Abdominal Aortic Aneurysm Screening With Ultrasound: Potential Cost Savings With Guideline Adherence and Review of Prior Imaging
Bibliographic record
Abstract
OBJECTIVE: To assess the appropriateness of abdominal aortic aneurysm (AAA) screening with ultrasound (US) and potential cost savings by adhering to guidelines and reviewing prior imaging. METHODS: Screening aortic US performed in Nova Scotia from January 1 to April 30, 2019, were reviewed. Patient sex, age, risk factors, and study result (negative, <2.5 cm; ectatic, 2.5-2.9 cm; positive for AAA, ≥3 cm) were recorded. Previous imaging tests were reviewed for the presence/absence of aortic ectasia or aneurysm. Appropriateness was based on the Canadian Task Force on Preventive Health Care (CTFPHC) and the Canadian Society of Vascular Surgery (CSVS) guidelines. The number of potentially averted US, subsequent missed positive findings, and cost savings (over the 4-month period) were calculated according to: 1) each guideline; and 2) each guideline combined with review of imaging done 0 to 5 years and 0 to 10 years previously. RESULTS: There were 17 (4.6%) of 369 ectatic aortas and 18 (4.9%) of 369 AAAs. The number of potentially averted examinations, missed ectatic aortas, missed AAAs, and cost savings were as follows, respectively: CTFPHC, 222 (60.2%) of 369, 8, 7, and CAD$20 501.70; CSVS, 117 (31.7%) of 369, 4, 2, and CAD$10 804.95. The model that would yield the greatest cost savings and fewest missed positive findings was the combination of CSVS guidelines with review of prior imaging within 5 years; this would avert 189 (51.2%) of 369 examinations, save CAD$17 454.15 over 4 months, and miss only 2 AAAs and 2 ectatic aortas. CONCLUSION: Over half of aortic US screening tests can be safely averted by adhering to CSVS guidelines and reviewing imaging performed within 5 years.
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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 | 0.008 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".