Thymic tumours: a single center surgical experience and literature review on the current diagnosis and management of thymic malignancies
Bibliographic record
Abstract
OBJECTIVE: This study aimed to provide an extensive overview of clinical and pathological findings along with various therapeutic options analyzing in addiction, retrospectively, the surgical outcomes of a single center cohort. BACKGROUND: Thymic neoplasms are rare thoracic tumors which commonly are located in the anterior mediastinum and are associated with a wide spectrum of clinical presentations. They may run an indolent course or could present a very aggressive biologic progression with infiltration of mediastinal structures and presence of distant metastases. The pathogenesis of these tumors is so far not completely clear. Several treatment modalities in a multidisciplinary setting have to be considered in order to provide the best treatment for patients affected by thymic tumors. METHODS: We conducted a retrospective cohort analysis of all patients who underwent surgery due to thymic tumor in a university hospital located in Switzerland (Bern University Hospital) and then we performed a narrative review of the English literature using PubMed, Embase, Cochrane Database of Systematic Reviews and Scopus. CONCLUSIONS: Minimally invasive techniques play an important role in the treatment of thymic tumors. A careful patients selection in a multidisciplinary setting is mandatory in order to offer the best treatment for patients affected by thymic tumors.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".