MBRS-10. RISK STRATIFICATION OF MEDULLOBLASTOMA ON THE BASIS OF MOLECULAR SUBGROUPING; AN EXPERIENCE FROM A SINGLE TERTIARY CARE CENTER FROM A DEVELOPING COUNTRY
Bibliographic record
Abstract
Medulloblastoma is the most common malignant child hood brain tumor. It has been risk stratified on the basis of clinical and histological types and recently as 4 molecular subgroups which includes (WNT, SHH, Group 3 and group 4). There is very scarce data available on the molecular subgrouping of medulloblastoma from developing countries. This is the only data from Pakistan on the molecular subgrouping in children with medulloblastoma. All children (0 – 16 years) with Medulloblastoma from April 2014 till December 2016 at Aga Khan University Hospital were reviewed retrospectively for clinical data, histology, molecular sub grouping, risk stratification, management and outcome. Biopsy samples of patients were sent for molecular sub grouping to Hospital for Sick Children, Toronto as a part of twinning program with Aga Khan University Hospital. 19 children were included in the study. Molecular subgrouping was done for 14 patients and showed Group 4 in 5, SHH in 4, WNT in1 patient. There were no patients with Group 3 and the subgrouping was inconclusive in 4 patients. Risk stratification based on extent of resection, metastasis and molecular subgrouping showed that there was only one low risk patient. There were 6 patients who were standard risk, 11 patients in high risk and 1 patient in very high risk group. There were 14 patients who were treated with chemo radiation followed by maintenance chemotherapy. This is the only study from Pakistan. Twinning programs can significantly help in proper diagnosis and management of pediatric brain tumors in LMIC.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".