S1988 Knowledge, Skills, and Confidence Gaps in Diagnosis of Primary Biliary Cholangitis Among Gastroenterologists and Advance Practice Providers Practicing in Gastroenterology or Hepatology
Notice bibliographique
Résumé
Introduction: Primary biliary cholangitis (PBC) is a chronic progressive inflammatory disease that can result in end-stage liver disease. Optimal care for PBC patients requires a multidisciplinary team approach to slow down disease progression and improve patients’ quality of life. This mixed-method study aimed at identifying knowledge, skill and confidence gaps of HCPs in ensuring an adequate PBC diagnosis. Methods: Semi-structured qualitative 45-minute interviews and a 15-minute quantitative survey were conducted within 4 sub-specialties: hepatologists, general gastroenterologists (GIs), primary care providers (PCPs) and advanced practice practitioners in GI/hepatology (APPs). The participants needed to be active in clinical practice in US , with a minimum of 2 years of experience. To ensure the focus on non-expert HCPs, a maximum PBC patient caseload was set for recruitment (20 patients/year for GIs, hepatologists and APPs; 10 patients over past 5 years for PCPs). Qualitative data underwent thematic analysis, quantitative data were analyzed using sub-group analysis with chi-square tests, and all data were triangulated in final analysis. This abstract will mainly focus on the gaps of GIs and APPs. Results: A total of 24 HCPs (6/sub-specialty) participated in interviews and 160 (40/sub-specialty) participated in survey. Mixed-method findings included difficulties with timely diagnosis and differentiating PBC from other liver diseases. Specifically, suboptimal skills (GIs 32%; APPs 100%) and suboptimal confidence (GIs 32%; APPs 100%) in recognizing liver enzyme patterns that suggest PBC were identified (Table 1). For APPs, the study also identified suboptimal skills (72%) and confidence (92%) in distinguishing between cholestatic and hepatocellular patterns in liver function tests, and in interpreting the significance of elevated alkaline phosphatase levels (suboptimal skills 92%; confidence 97%). APPs also had suboptimal skills (47%) and confidence (75%) in utilizing anti-mitochondrial antibody (AMA) testing effectively for diagnosis. GIs (30%) reported suboptimal confidence in employing advanced diagnostic tools like liver biopsy in PBC. Conclusion: Knowledge, skills and confidence gaps were identified among GIs and APPs, pointing to an opportunity for educational interventions for GI providers. Focused educational interventions in recognizing PBC symptom patterns and appropriate use of diagnostic tools will improve care for patients living with PBC. Table 1. - Percentages of Participants, by Profession/Specialty, whose Self-Reported Skills or Confidence Levels Were Considered as Sub-Optimal Survey item S / C* APPs (GI + Hepatology) GIs Hepatologists PCPs Statistical results Recognizing liver enzyme patterns that suggest PBC S 100% 32% 2% 72% (n=160, P< .001) C 100% 32% 5% 77% (n=160, P< .001) Interpreting the significance of elevated alkaline phosphatase (ALP) levels in PBC patients S 92% 7% 0% 45% (n=160, P< .001) C 97% 17% 2% 62% (n=160, P< .001) Distinguishing between cholestatic and hepatocellular patterns in liver function tests (LFTs) for PBC S 72% 2% 0% 55% (n=160, P< .001) C 92% 12% 0% 62% (n=160, P< .001) Utilizing anti-mitochondrial antibody (AMA) testing effectively for diagnosing PBC S 47% 7% 2% 35% (n=160, P< .001) C 75% 7% 2% 47% (n=160, P< .001) Employing advanced diagnostic tools like liver biopsy in PBC S 94% 25% 0% n/a (n=97**, P< .001) C 95% 30% 0% n/a (n=100**, P< .001) * S = self-reported suboptimal skills (1-3 on 5-point scale); C = self-reported suboptimal confidence level (0-75 on 100-point slider scale). ** Lower sample size as question not asked to PCPs.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,002 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».