MétaCan
Menu
← Back to cohort
Record W2532570220 · doi:10.1017/cjn.2016.360

PS1 - 182 Epidemiology and Review of the Trends of Brain Tumors in Children under the Age of 3: A Report from the Canadian Pediatric Brain Tumour Consortium

2016· article· en· W2532570220 on OpenAlexaffvenueabout
Salini Thulasirajah, David Johnston, Daniel Keene

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2016
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsMcMaster UniversityChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsMedicineMedulloblastomaEpidemiologyVomitingPediatricsIncidence (geometry)Brain tumorRetrospective cohort studyCentral nervous systemSurgeryPathologyInternal medicine

Abstract

fetched live from OpenAlex

To describe the epidemiology of children under age 3 diagnosed with central nervous system tumors in Canada. Case ascertainment: Retrospective observation study of children under age 3 diagnosed with neoplasm involving the central nervous system between 1990 and 2005 at 13 of the Canadian pediatric oncology centres. Results: Case ascertainment was 573 persons. Below 6 months of age at diagnosis, no gender difference was seen and the commonest location of tumor was supratentorial. Embryonal tumors were the commonest, increased head circumference and vomiting were the commonest presenting symptom and survival rates were poor. Over 6 months of age at diagnosis, male predominance occurred and commonest location of tumor was the cerebellum. The commonest tumor was astrocytic, vomiting was the commonest presenting symptom and survival was better than in the under 6-month age group. Conclusion: Over the study period, the incidence rate and degree of resection remained stable. A trend to increased survival in children with ependymal tumors occurred; while, with medulloblastoma, survival decreased.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.073
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.016
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.042
GPT teacher head0.299
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), 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
Published2016
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques→Same topicGlioma Diagnosis and Treatment→French-language works237,207→