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Record W4321497935 · doi:10.14218/jctp.2022.00028

Testicular Germ Cell Tumors with Somatic-type Malignancy

2023· article· en· W4321497935 on OpenAlexaff
Jiaming Fan, Yong Guan, Charles C. Guo, Gang Wang

Bibliographic record

VenueJournal of Clinical and Translational Pathology · 2023
Typearticle
Languageen
FieldMedicine
TopicTesticular diseases and treatments
Canadian institutionsInstitute of Cancer Research
Fundersnot available
KeywordsMalignancySomatic cellTesticular cancerGerm cell tumorsPathologySeminomaBiologyCancerMedicineOncologyInternal medicineChemotherapy

Abstract

fetched live from OpenAlex

Testicular cancer accounts for ∼1% of all cancers in men worldwide, with over 90% of testicular cancers being germ cell tumors (GCTs). Since the introduction of multimodal therapy, testicular GCTs have been among the most curable solid tumors. However, some patients may develop late relapse, which is defined as recurrence at least two years after the initial complete remission. Late recurrence is particularly common in patients with teratomatous GCTs and is associated with somatic-type malignancy (SM) development. Approximately 2.5–8.0% of testicular GCT patients may develop SM, a distinct secondary component that resembles cancers seen in other organs and tissues. The histological subtypes of SM are diverse and may show morphological features of sarcomas, carcinomas, embryonic-type neuroectodermal tumors, nephroblastomas, hematologic malignancies, or a combination of different forms. Several studies have demonstrated that the development of SM in testicular GCTs, particularly at metastatic sites, is associated with a poor prognosis. In the current review, we discuss the concept of GCTs with SM, the diagnostic criteria, the common histological subtypes, the pathogenesis, and the clinical outcomes of GCT patients with SM.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.047
GPT teacher head0.363
Teacher spread0.316 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2023
Admission routes1
Has abstractyes

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