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S1988 Knowledge, Skills, and Confidence Gaps in Diagnosis of Primary Biliary Cholangitis Among Gastroenterologists and Advance Practice Providers Practicing in Gastroenterology or Hepatology

2024· article· en· W4403720929 on OpenAlexaff
Sophie Peloquiin, Kris V. Kowdley, Edward Mena, Andrea A. Gossard, Fan Lü, Lyota Bonyeme, Patrice Lazure

Bibliographic record

VenueThe American Journal of Gastroenterology · 2024
Typearticle
Languageen
FieldMedicine
TopicGallbladder and Bile Duct Disorders
Canadian institutionsAxdev Group (Canada)
Fundersnot available
KeywordsMedicineHepatologyInternal medicineGastroenterologyPrimary careFamily medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.829

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.289
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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