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Primary central nervous system lymphoma in children: Insights from the three-year experience at the largest public-sector pediatric oncology center in Pakistan

2025· article· en· W4417220298 on OpenAlexaff
Rahat-Ul-Ain, Fiza Ismail, Laeeq-ur-Rehman, Rabia Qaiser, Alia Ahmad, Mahwish Faizan

Bibliographic record

VenuePakistan Journal of Medical Sciences · 2025
Typearticle
Languageen
FieldMedicine
TopicCNS Lymphoma Diagnosis and Treatment
Canadian institutionsContinental (Canada)
Fundersnot available
KeywordsPediatric oncologyPrimary central nervous system lymphomaAbandonment (legal)Surgical oncologyCenter (category theory)Hodgkin lymphomaCentral nervous system

Abstract

fetched live from OpenAlex

Objective: Primary CNS lymphoma (PCNSL) in children is a rare disease, and this study aimed to document the experience of dealing with this in our geographical and resource settings. Methodology: It was an ambidirectional cohort study conducted at the Department of Pediatric Hematology/Oncology and the Department of Pediatric Neurosurgery at the University of Child Health Sciences, The Children's Hospital Lahore, Pakistan from March 2024 to December 2024. This study included seven suspected and confirmed cases of PCNSL in children under the age of 16 years who presented to a public-sector specialized center in Pakistan over the three-year study period. Results: A total of seven cases of suspected PCNSL were included in the study. The median age at presentation was eight years, with a female-to-male ratio of 2.5:1. Most commonly presenting with a focal neurological deficit, with a median duration of symptoms of 12 weeks, and a median Lansky performance score of 50. Only 57% (04) of patients underwent surgical resection followed by adjuvant chemotherapy. Overall, 57% (03) of patients died, 29% (02) were lost to follow-up, and only 14% (01) are under treatment. Conclusion: PCNSL in children is equally rare in our part of the world, but has a dismal survival rate. Timely surgical intervention, improved supportive care, and a reduction in treatment abandonment might improve the prognosis.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.433

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.317
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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