The Separation of Benign and Malignant Mesothelial Proliferations
Bibliographic record
Abstract
CONTEXT: The separation of benign from malignant mesothelial proliferations is crucial to patient management but is often a difficult problem for the pathologist. OBJECTIVE: To review the pathologic features that allow separation of benign from malignant mesothelioma proliferations, with an emphasis on new findings. DATA SOURCES: Literature review and experience of the authors. CONCLUSIONS: Invasion is still the most reliable indicator of malignancy. The distribution and amount of proliferating mesothelial cells are important in separating benignity from malignancy, and keratin stains can be valuable because they highlight the distribution of mesothelial cells. Hematoxylin-eosin examination remains the gold standard, and the role of immunochemistry is extremely controversial; we believe that at present there is no reliable immunohistochemical marker of malignancy in this setting. Mesothelioma in situ is a diagnosis that currently cannot be accurately made by any type of histologic examination. Desmoplastic mesotheliomas are characterized by downward growth of keratin-positive spindled cells between S100-positive fat cells; some cases of organizing pleuritis can mimic involvement of fat, but these fat-like spaces are really S100-negative artifacts aligned parallel to the pleural surface. Fluorescence in situ hybridization on tissue sections to look for homozygous p16 gene deletions is occasionally useful, but many mesotheliomas do not show homozygous p16 deletions. Equivocal biopsy specimens should be diagnosed as atypical mesothelial hyperplasia and another biopsy requested if the clinicians believe the process is malignant.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".