HGG-03. PREVALENCE OF BIALLELIC MISMATCH REPAIR DEFICIENCY IN CHILDREN WITH MALIGNANT GLIOMA TREATED AT KING FAHAD MEDICAL CITY (KFMC)
Bibliographic record
Abstract
Hereditary Constitutional mismatch repair -deficiency (CMMR-D) caused by biallelic mutations in one or more MMR genes a cancer predisposition syndrome with features of neurofibromatosis type 1, often café-au-lait macule, development of different types of cancers in multiple organs. Given the high rate of consgunity in our population, we measure the prevalence of CMMR-D in our pediatric population with gliomas treated at our institution. We retrospectively analyzed demographic, immunohistochemistry, and molecular data for 31 cases diagnosed to have HGG and at KFMC between December 2006 and December 2016. Of the 31 patients (64.5 %) were boys and (35.5 %) girls. The median age at diagnosis was 11 years (range 2 - 17years). The most common pathological subtype was: Glioblastoma (58%) followed by Anaplastic Astrocytoma (19.4%). MMRD was observed in 19.4% of cases. There was no significant difference in the median survival time between MMR-D deficiency cases (1.78 years) and other cases (1.27 year). This is the first study to measure prevalence of CMMRD in Saudi Arabia which is known to have a high rate of consanguineous families. Our data showed around 20% of high grade glioma patients has CMMRD which is confirmed by two testing approaches: immunohistochemistry and molecular. In the era of immunotherapy, identifying CMMRD cases is critical as it opens new avenues for treatment options with check point inhibitors. Limitations in our study including small sample size and retrospective design and future prospective studies with larger sample size will be needed to confirm our results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".