Guidelines for Pathologic Diagnosis of Malignant Mesothelioma: 2012 Update of the Consensus Statement from the International Mesothelioma Interest Group
Why this work is in the frame
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Bibliographic record
Abstract
CONTEXT: Malignant mesothelioma (MM) is an uncommon tumor that can be difficult to diagnose. OBJECTIVE: To provide updated practical guidelines for the pathologic diagnosis of MM. DATA SOURCES: Pathologists involved in the International Mesothelioma Interest Group and others with an interest in the field contributed to this update. Reference material includes peer-reviewed publications and textbooks. CONCLUSIONS: There was consensus opinion regarding (1) distinction of benign from malignant mesothelial proliferations (both epithelioid and spindle cell lesions), (2) cytologic diagnosis of MM, (3) key histologic features of pleural and peritoneal MM, (4) use of histochemical and immunohistochemical stains in the diagnosis and differential diagnosis of MM, (5) differentiation of epithelioid MM from various carcinomas (lung, breast, ovarian, and colonic adenocarcinomas, and squamous cell and renal cell carcinomas), (6) diagnosis of sarcomatoid mesothelioma, (7) use of molecular markers in the diagnosis of MM, (8) electron microscopy in the diagnosis of MM, and (9) some caveats and pitfalls in the diagnosis of MM. Immunohistochemical panels are integral to the diagnosis of MM, but the exact makeup of panels used is dependent on the differential diagnosis and on the antibodies available in a given laboratory. Immunohistochemical panels should contain both positive and negative markers. It is recommended that immunohistochemical markers have either sensitivity or specificity greater than 80% for the lesions in question. Interpretation of positivity generally should take into account the localization of the stain (eg, nuclear versus cytoplasmic) and the percentage of cells staining (>10% is suggested for cytoplasmic membranous markers). These guidelines are meant to be a practical reference for the pathologist.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it