The role of micro-RNAs in management of germ cell tumors: future directions
Bibliographic record
Abstract
PURPOSE OF REVIEW: miRNAs 371 and 302/367 clusters are abundantly secreted in the blood of patients with active germ cell malignancy (aGCM), both seminoma and nonseminoma. The serum concentration of those micro-RNAs correlates with tumor burden and to the activity of specific treatments; therefore, representing attractive biomarkers for the diagnosis and follow-up of patients with germ cell tumors. This review summarizes the most relevant evidence supporting their clinical validity in germ cell tumors. RECENT FINDINGS: Several retrospective studies have reported high sensitivity and specificity of those micro-RNAs in identifying aGCM prior to the orchiectomy or in patients with metastatic germ cell tumor prior to or during chemotherapy. Most recently, few prospective studies have confirmed their clinical validity during the follow-up of patients after surgery and/or chemotherapy. Large studies are panned across the spectrum of germ cell tumors to assess their clinical utility and several efforts to identify biomarkers of teratoma are underway. SUMMARY: The integration of those micro-RNAs in the management of germ cell tumors has the potential to refine the therapeutic decision, especially in some clinical situations characterized by high uncertainty, such as clinical stage I, clinical stage IIA with normal tumor markers and residual disease postchemotherapy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".