Affordable panel of techniques for prediction of molecular classification of a series of medulloblastomas in a reference pediatric hospital in Colombia
Bibliographic record
Abstract
Abstract Molecular classification of medulloblastomas helps in improving risk-stratification. However, application in routine practice remains a challenge in low and middle-income countries. In Colombia, children often have delayed and uncomplete diagnosis. We underwent a retrospective analysis of 49 cases of medulloblastoma treated between 2009 and 2017 in a reference pediatric hospital in Bogotá, Colombia. This manuscript reports the use of a immunohistochemical plus PCR panel to distinguish SHH, WNT, and non-SHH/WNT tumors and details their clinical and pathological features. We analyzed Beta-catenin, p75NTR, PIGU, OTX2, YAP1 and P53 by immunohistochemistry and performed PCR for C-myc and N-myc amplification. We found a high percentage of SHH tumors and a high prevalence of desmoplastic-nodular tumors in our series. The male: female ratio was different from reported in other latitudes. We believed it would be important to complement these results by new generation sequencing and the gold standard in the medulloblastoma diagnosis, the methylation analysis. However, the panel we propose is useful to predict the molecular group. This is the first medulloblastoma case series in Colombia.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".