GC-03AREAS OF NON-CONSENSUS CHALLENGING THE MANAGEMENT OF INTRACRANIAL GERM CELL TUMOURS (ICGCT)
Bibliographic record
Abstract
PURPOSE: The international Delphi approach defined areas of consensus which streamlined ICGCT management [1]. However, areas of different practice between nations were identified, in particular relating to bifocal lesions, CSF cytology and tumour-marker thresholds. For example, European/North-American working-groups treat ICGCT patients with positive CSF cytology with craniospinal irradiation, whereas in Japan, cytology doesn't influence treatment, with no apparent detriment to survival. Furthermore, initial ICGCT management generally involves debulking surgery in Japan, while biopsy is favoured in Europe/North-America. Comprehensive histological assessment prior to adjuvant treatment therefore allows the Japanese working-group a unique opportunity to study correlations between IGGCT composition and tumour marker levels. For example, differing HCG thresholds used for distinguishing germinoma from choriocarcinoma reflect a lack of evidence. METHODS: Key non-consensus questions: 1. Retrospective review of histology of bifocal lesions and their association with presence/absence of diabetes-insipidus and tumour marker (AFP/HCG) levels by the Japanese working-group. 2. Retrospective review in Japanese institutions to identify patients with positive CSF cytology who received whole ventricular radiation with or without prior chemotherapy, their associated relapse pattern and survival. 3. Comparison of HCG levels, tumour volume and percentage histological composition of germinoma/choriocarcinoma components. RESULTS: These reviews will highlight relative advantages/disadvantages of different management approaches, suggest treatment strategies associated with least risk but optimal survival/quality-of-life and identify an HCG threshold to reliably distinguish germinoma from non-germinomatous ICGCTs containing choriocarcinoma components. CONCLUSION: These comparisons of different ICGCT management approaches are likely to assist optimal patient stratification and treatment and ultimately translate into improved clinical outcomes. [1]Murray MJ et al, Lancet Oncology 2015;16:e470-7.
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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 | 0.071 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".