Differential Diagnosis of Benign and Malignant Mesothelial Proliferations on Pleural Biopsies
Bibliographic record
Abstract
CONTEXT: Although much of the pathology literature focuses on differential diagnosis of diffuse malignant mesothelioma from other types of cancer, the primary diagnostic challenge facing the pathologist is often whether a mesothelial proliferation on a pleural biopsy represents a malignancy or a benign reactive hyperplasia. DESIGN: Based on previous medical publications, extensive personal consultations, and experience on the United States-Canadian Mesothelioma Reference Panel and the International Mesothelioma Panel, salient information was determined about interpretation of benign versus malignant mesothelial proliferations on pleural biopsies. RESULTS: Differentiation of benign reactive mesothelial hyperplasia from diffuse malignant mesothelioma is often difficult. Benign reactive mesothelial hyperplasia may mimic many features ordinarily associated with malignancy, and diffuse malignant mesothelioma may be cytologically bland. Entrapment of benign reactive mesothelial cells within organizing pleuritis may mimic tissue invasion. CONCLUSIONS: Various histologic clues favor a benign over a malignant mesothelial proliferation and vice versa. Invasion is the most reliable criterion for determining that a mesothelial proliferation is malignant. When there is any doubt that a pleural biopsy represents a malignancy, we recommend a diagnosis of atypical mesothelial proliferation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".