Advances in the diagnosis, treatment and prognosis of malignant pleural mesothelioma.
Bibliographic record
Abstract
Malignant pleural mesothelioma (MPM) is a rare cancer originated from pleural mesothelial cells. MPM has been associated with long-term exposure to asbestos. The prognosis of MPM is poor due to the difficulty of making diagnosis in the early stage, the rapid progression, the high invasiveness and the lack of effective treatment. Although the incidence of MPM is low in China to date, it has a tendency to increase in the coming years. The variety of clinical features may cause the delay of diagnosis and high rate of misdiagnosis. The diagnosis of MPM is based on biopsy of the pleura and immunohistochemistry. As China has become the largest country in the consumption of asbestos, it would give rise to a new surge of MPM in the future. The current treatment of MPM is multimodality therapy including surgery, radiotherapy, chemotherapy and immunotherapy. Two surgical procedures are commonly applied: extrapleural pneumonectomy (EPP) and pleurectomy/decortication (P/D). Three dimensional conformal radiotherapy is used to denote a spectrum of radiation planning and delivery techniques that rely on the 3D imaging to define the tumor. Cisplatin combined with pemetrexed (PEM) is the first-line chemotherapy for MPM. The principal targets in immunotherapy include T cells (Treg), CTLA-4 and PD-1. The diagnosis, treatment and prognosis still remain a major challenge for clinical research and will do so for years to come.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".