Diagnosis of Pleural Mesothelioma: Is Everything Solved at the Present Time?
Bibliographic record
Abstract
Ranked high in worldwide growing health issues, pleural diseases affect approximately one million people globally per year and are often correlated with a poor prognosis. Among these pleural diseases, malignant pleural mesothelioma (PM), a neoplastic disease mainly due to asbestos exposure, still remains a diagnostic challenge. Timely diagnosis is imperative to define the most suitable therapeutic approach for the patient, but the choice of diagnostic modalities depends on operator experience and local facilities while bearing in mind the yield of each diagnostic procedure. Since the analysis of pleural fluid cytology is not sufficient in differentiating historical features in PM, histopathological and morphological features obtained via tissue biopsies are fundamental. The quality of biopsy samples is crucial and often requires highly qualified expertise. Since adequate tissue biopsy is essential, medical or video-assisted thoracoscopy (MT or VATS) is proposed as the most suitable approach, with the former being a physician-led procedure. Indeed, MT is the diagnostic gold standard for malignant pleural pathologies. Moreover, this medical or surgical approach can allow diagnostic and therapeutic procedures: it provides the possibility of video-assisted biopsies, the drainage of high volumes of pleural fluid and the administration of sterile calibrated talcum powder under visual control in order to achieve pleurodesis, placement of indwelling pleural catheters if required and in a near future potential intrapleural therapy. In this context, dedicated diagnostic pathways remain a crucial need, especially to quickly and properly diagnose PM. Lastly, the interdisciplinary approach and multidisciplinary collaboration should always be implemented in order to direct the patient to the best customised diagnostic and therapeutic pathway. At the present time, the diagnosis of PM remains an unsolved problem despite MDT (multidisciplinary team) meetings, mainly because of the lack of standardised diagnostic work-up. This review aims to provide an overview of diagnostic procedures in order to propose a clear strategy.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".