Fishing for BASS: Surveying Primary Physicians in the Barriers to Aortic Screening Study
Bibliographic record
Abstract
Introduction In response to published guidelines for abdominal aortic aneurysm (AAA) screening, primary care physicians were surveyed to determine attitudes and identify barriers to screening. Methods Six hundred standardized, structured surveys were distributed to all primary care practitioners in a defined geographic area. Participation was voluntary, and results were anonymous. Results A total of 10.7% of surveys were returned. All questions were answered by >93% of respondents. A total of 71.9% of respondents were general practitioners; 94.4% worked in a community setting. 60.9% saw >11 male patients per week who were older than 65 years of age. Responses indicated support for identifying asymptomatic AAAs; only 4.7% thought their patients were too sick to undergo repair, 0% felt their patients would be unwilling to undergo repair, and 0% felt the risk of rupture was too small to justify repair. Access to vascular surgical services was available to more than 75% in the hospital closest to them, and to 100% in the city in which they practice. A total of 42.2% were aware of recommendations regarding AAA screening, and 65.6% of physicians routinely screened eligible patients for AAAs. Screening for other diseases was more frequent. Respondents routinely screened their patients for breast cancer (79.1%), prostate cancer (80.5%), colon cancer (80.9%), and hypertension (83.7%); 42.9% routinely screened for peripheral artery disease. Conclusion Screening for AAAs lags significantly behind other major screening programs. Although primary practitioners are routinely exposed to the target population, a minority of patients are screened. Neither access to a vascular surgeon nor knowledge about the importance of AAAs appears to be limiting factors. Despite recent publicity, almost 60% of primary care physicians remain unaware of screening guidelines for AAAs. Of those who were aware of guidelines, only one third follow them. Further research and education is required to increase the efficacy of screening.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".