Why are rare diseases underdiagnosed? A clinical management study on detection of primary biliary cholangitis in primary care.
Bibliographic record
Abstract
Background: There are about 7,000 rare diseases that affect 10% of the world population. Primary biliary cholangitis, an autoimmune chronic liver disease of the interlobular bile ducts, is one of the most common causes of chronic cholestasis. However, it is a rare, often underdiagnosed and undertreated, disease which can lead to cirrhosis and liver failure. We aimed to assess the proportion of undetected primary biliary cholangitis patients in primary care through a clinical management process. Methods: We made two extractions of the clinical data concerning liver diseases, risk factors and laboratory tests from the databases of a sample of general practitioners, with a check and correction of mistakes. The clinical data of the patients without liver disease and major risk factors, and with serum Alkaline Phosphatase above the laboratory reference values, were re-evaluated by each general practitioner with an expert gastroenterologist. The patients with elevated Alkaline Phosphatase values and without evidence of intrahepatic or extrahepatic causes of cholestasis were considered suspected for primary biliary cholangitis and assessed for antimitochondrial antibodies test and specialist' s evaluation, according to present guidelines. Results: A total of 20,480 adults attending 14 general practitioners in the province of Brescia, Northern Italy, were included in the study. Nine patients had a prior primary biliary cholangitis diagnosis, with a prevalence of 43.9/100000. After excluding 2094 (10.2%) patient with liver diseases or other causes of cholestasis, 121 subjects with Alkaline Phosphatase above the reference values were re-evaluated by the general practitioners and gastroenterologist, and 27 patients without symptoms or signs of cholestasis were considered suspected for primary biliary cholangitis: 9 of them were tested for antimitochondrial antibodies, and three new primary biliary cholangitis cases were detected (+33%). Discussion and Conclusions: This study shows that there is a not negligible burden of undetected cases of adult rare diseases that can be diagnosed in primary care, through a disease management procedure, without modifying the routine clinical practice.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".