Twelve Years of Experience in the Management of Testicular Germ Cell Tumors at a Referral Center in Portugal
Bibliographic record
Abstract
BACKGROUND: Testicular germ cell tumors (TGCT) are generally rare but quite frequent in young males. Guidelines are well established for their management. METHODS: We present the first report from Portugal on clinical, histological, treatment modalities and outcomes of a population with TGCT. Data was retrospectively analyzed for the 1996 through 2008 period, applying a previous internally validated protocol. RESULTS: Seventy nine patients with TGCT were identified, 40.5% had seminomatous and 59.5% nonseminomatous tumors. Incidence rates were higher among males in their twenties and thirties. Pain and swelling testis were the most common symptoms and microlithiasis was detected in 20.3% of patients. Lower stages were more frequent in seminomatous tumors. Orchiectomy was done in all patients and further therapy was performed by guidelines recommendations in 86.1% of them. Hematological toxicity was found in 44.3% of the population studied and free disease survival rates were at 88.6%. CONCLUSIONS: This retrospective study corroborates the European Western country trends concerning TGCT. Mortality was only seen in nonseminomatous TGCT group. Good risk and lower TGCT stages have no deaths reported. Public health campaigns should be undertaken to guide patients to seek medical advice earlier in the course of the disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".