015 Achieving consensus on overused tests : the example of hepatic steatosis
Bibliographic record
Abstract
Metabolic Associated Steatotic Liver Disease (MASLD), previously known as ‘’Fatty Liver’’, has emerged as a common clinical entity and is a source of overdiagnosis. Recent European and American specialist guidelines incite screening, active case finding and follow-ups of this condition in primary care. Benefits of these extensive diagnostic workups are often not evidence based. Few studies show any positive impact on morbidity or mortality and most recommendations are ‘Expert Opinion’. Screening for MASLD is quickly becoming a driver for time consuming low-value care in busy outpatient clinics. In response to a clinician-identified struggle to provide high-value, evidence-based care, a panel was formed to tackle the current evidence behind the guidelines, the impact of their full application on a primary care practice and expected repercussions on clinical-decisions and patient-important outcomes. The panel was composed of primary care physicians working in hospital and community settings and a nurse practitioner in adult care. Each member sought out evidence-based literature in medical databases to answer PICO questions focused on the impact of MASLD screening, follow-up and treatment. Furthermore, the panel critically appraised the two recent guidelines, using tools such as G-Trust. Information gathered was discussed and analyzed as a group to form recommendations created by and for primary care. This seminar aims to describe the panel’s findings. We will review the definition of MASLD and reasons behind why it has become a major health focus in many developed countries. Current European and American guidelines will be presented and critically appraised. We use the concept of time needed to treat and a clinical decision making 1000-person-tool to highlight the resource-consuming impact the guidelines can have on primary care workloads, particularly in the context of doubtful patient expected benefits. The seminar will conclude with four ‘Choosing Wisely’ recommendations and two algorithms to assist primary care clinicians in dealing with case-findings of liver steatosis and abnormal hepatic enzymes. By the end of this seminar participants will: Identify how MASLD is a common source of overdiagnosis Appreciate how practice guidelines related to MASLD are based on little to no evidence Identify key recommendations for primary care physicians regarding MASLD to reduce overdiagnosis, as well as low-value testing and treatment
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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 | 0.208 | 0.304 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.007 | 0.020 |
| Research integrity | 0.014 | 0.021 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".