Inter-observer Variability in Histomorphological Evaluation of Non-neoplastic Liver Biopsy Tissue and Impact of Clinical Information on Final Diagnosis in Shahid Beheshti University of Medical Sciences Affiliated Hospitals
Bibliographic record
Abstract
Scan to discover onlineBackground & Objective: Liver biopsy is the main method for grading and staging liver disorders, but the effects of clinical information and optimal biopsy specimen size on interpretation remain contentious.The aim of the study was to evaluate the impact of clinical information and quality of liver specimen on inter-observer agreement for liver disease.Methods: A total of 289 consecutive biopsy specimens from 2010 to 2017 were re-evaluated by five pathologists using the modified Ishak and non-alcoholic fatty liver diseases (NAFLD) activity score (NAS) systems.Detailed clinical information was extracted from medical records of patients and the size of all liver biopsy samples was recorded.Results: Full agreement between primary diagnosis and final diagnosis was obtained in 214 cases (74%).The remaining cases, namely 22 (7.6%) and 53 (18.3%) biopsies had minor and major diagnostic discrepancies, respectively.The results showed that the overall agreement was significantly higher in cases with complete clinical information than patients without any clinical information and even with partial clinical information (P<0.001).Interestingly, no significant difference in inter-observer agreement was achieved with a length over 20 mm (P=0.181).However, the inter-observer variation significantly decreased when the number of portal tract was more than 10 (P=0.001).Conclusion: This study identified the impact of clinical information and the number of portal tracts as the key factors to diagnosis.Therefore, request forms for liver biopsies should always be accompanied with the clinical history.Moreover, adequacy of biopsy specimens is very useful for accurate evaluation of samples by pathologists.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".