Impact of dedicated pediatric neuro‐oncological services in a developing country: A single‐institution, Pakistani experience
Bibliographic record
Abstract
INTRODUCTION: Brain tumors are the most common solid neoplasms and the second most common malignancy in the pediatric age group. Due to the complexity of their management, pediatric central nervous system (CNS) tumors are not a priority in low- and middle-income countries (LMICs). METHODS: In an attempt to improve the survival rate and overall care, we introduced a dedicated pediatric neuro-oncology service in our institute and evaluated its impact by dividing the pre- and post-era into two cohorts and comparing them: 1998-2013 (16 years: cohort A) and 2014-2019 (6 years: cohort B, after the start of dedicated neuro-oncology services). RESULTS: We observed that after the implementation of a proper neuro-oncology service, the proportion of patients treated with curative intent increased, and survival improved in cohort B. The patient volume also increased from 15.5 per year in cohort A to 44.8 per year in cohort B. The percentage of children given radiation therapy also increased significantly, while the proportion of children treated with chemotherapy remained stable. CONCLUSION: A dedicated multidisciplinary team trained and knowledgeable in the specialty of pediatric neuro-oncology can enhance and improve outcomes, and supportive care and help can provide good quality of life to children and their families with brain neoplasms.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".