S1988 Knowledge, Skills, and Confidence Gaps in Diagnosis of Primary Biliary Cholangitis Among Gastroenterologists and Advance Practice Providers Practicing in Gastroenterology or Hepatology
Bibliographic record
Abstract
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.
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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 | 0.007 | 0.028 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".