Fishing for BASS: Surveying Primary Physicians in the Barriers to Aortic Screening Study
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Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle