Review and update of uncommon primary pleural tumors: a practical approach to diagnosis.
Bibliographic record
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.
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.
How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".