Epidemiology and Outcomes of Midline Tumors in a Tertiary Care Hospital in Pakistan: A Retrospective Cohort of 10 years
Bibliographic record
Abstract
Introduction: Diffuse midline gliomas (DMGs) are among the most devastating pediatric cancers, accounting for approximately 20% of all pediatric central nervous system tumors.DMGs include all diffuse intrinsic pontine gliomas (DIPG), previously usually used for only pontine gliomas, to emphasize that these lesions are not solely centered in the brainstem.In this retrospective review, we aim to report the incidence and outcomes of all midline tumors in a tertiary care setup.Methodology: Data were collected retrospectively from the medical records at Aga Khan University Hospital between 2013 -2023.All patients <18 with midline tumors (brainstem, thalamus, and spinal cord) were reviewed; 102 patients were included.Few tumor samples were also sent to Sick Kids, Toronto, for molecular testing.Results: Our cohort represents 102 patients with midline tumors; a median age of 11 years (interquartile range (IQR): 7.75-15 years), with similar male-to-female ratio.Most patients presented with limb weakness and headache (median duration: 1.5 months, IQR: 1-4 months).The most common tumor site was brainstem, followed by spine and thalamus.Surgery was performed on 66 patients, including 2 DIPGs, 15 low-grade gliomas, 13 ependymomas, 8 high-grade gliomas, and 16 patients with other tumors.All of the patients diagnosed with DMG had H3K27 alteration on immunohistochemistry.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".