Pakistan National Guidelines for Pediatric High-Grade Gliomas
Bibliographic record
Abstract
Pediatric high-grade glioma (pHGG) is highly malignant central nervous system tumor and constitute 10% of the pediatric gliomas. Effective treatment needs a functioning multi-disciplinary team including pediatric neuro oncologist, neurosurgeon, neuroradiologist, neuropathologist and radiation oncologist. Despite surgical resection, radiotherapy and chemotherapy, most HGG will recur resulting in early death. A significant proportion of HGG occurs in context of cancer predisposition syndromes like Constitutional Mismatch Repair Deficiency (CMMRD) also known as Biallelic Mismatch Repair Deficiency (bMMRD) characterized by high mutational burden. The incidence of HGG with CMMRD is one per million patients. bMMRD is caused by homozygous germline mutations in one of the four Mis Match Repair (MMR) genes (PMS2, MLH1, MSH2, and MSH6). The use of TMZ is now avoided in CMMRD related HGG due to its limited response and known ability to increase the accumulation of somatic mutations in these patients, increasing the risk of secondary tumors. HGG should be managed under the care of multidisciplinary team to receive optimum treatment. This is particularly important for low middle-income countries (LMIC) with limited resources like Pakistan. doi: https://doi.org/10.12669/pjms.39.5.6300 How to cite this: Bashir F, Qureshi BM, Minhas K, Tabori U, Bouffet E, Hawkins C, et al. Pakistan National Guidelines for Pediatric High-Grade Gliomas. Pak J Med Sci. 2023;39(5):1548-1554. doi: https://doi.org/10.12669/pjms.39.5.6300 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".