Review and Update of Uncommon Primary Pleural Tumors: A Practical Approach to Diagnosis
Bibliographic record
Abstract
Abstract Objective.—We address the current classifications and new changes regarding uncommon primary pleural tumors. Primary pleural tumors are divided according to their behavior and are discussed separately as benign tumors, tumors of low malignant potential, and malignant neoplasms. Data Sources.—Current literature concerning primary pleural neoplasms was collected and reviewed. Study Selection.—Studies emphasizing clinical, radiological, or pathologic findings of primary pleural neoplasms were obtained. Data Extraction.—Data deemed helpful to the general surgical pathologist when confronted with an uncommon primary pleural tumor was included in this review. Data Synthesis.—Tumors are discussed in 3 broad categories: (1) benign, (2) low malignant potential, and (3) malignant. A practical approach to the diagnosis of these neoplasms in surgical pathology specimens is offered. The differential diagnosis, including metastatic pleural neoplasms, is also briefly addressed. Conclusions.—Uncommon primary pleural neoplasms may mimic each other, as well as mimic metastatic cancers to the pleura and diffuse malignant mesothelioma. Correct diagnosis is important because of different prognosis and treatment implications for the various neoplasms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".