The Separation of Benign and Malignant Mesothelial Proliferations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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