Is the International Germ Cell Consensus Classification (IGCCC) sufficiently predictive in 2015?
Bibliographic record
Abstract
387 Background: The IGCCC has been an invaluable tool to guide clinical trial development in disseminated germ cell tumors. This classification was developed in the early 1990s. The data were abstracted from records of pts treated between 1975 and 1990 and > 100 institutions submitted data. This analysis resulted in the development and validation of a simple system based on clinically derived parameters. Three risk groups were identified for disseminated nonseminoma.; “good risk” group with a predicted 5 year overall survival (OS) > 90%, ‘intermediate risk” with a 5 yr OS of 75% and “poor risk” with a predicted 48% 5 yr OS. Recently, a number of clinical trials and large institutions have reported outcomes in intermediate and poor risk disseminated germ cell tumors. Outcomes reported exceed IGCCC predictions. We hypothesize that the IGCCC substantially underestimates outcomes in the modern era. Further we speculate that a re-analysis of existing clinical trial data would be fruitful in predicting outcomes for disseminated germ cell tumors in the 21st century. Methods: Reports from large randomized clinical trials reporting outcomes in intermediate and poor risk disseminated germ cell tumors were reviewed and estimates of Progression Free and Overall survival made. Results: See Table. Conclusions: Compared to the IGCCC predictions based on data from 25-40 years ago, there appears to be improved overall survival in disseminated germ cell tumors in the modern era. Intermediate risk and poor risk disease appears to have OS exceeding 80-85% and 75% respectively. A more accurate prediction of outcomes with standard treatments should inform clinical trial design going forward. [Table: see text]
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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 | 0.013 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".