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Record W2592704154 · doi:10.1097/mph.0000000000000798

Medulloblastoma With Extensive Nodularity:Tailored Therapy in a Low-resource Setting

2017· article· en· W2592704154 on OpenAlexaff
Paula Aristizabal, Luke Burns, Rebeca Rivera-Gómez, Michael L. Levy, William Roberts, John R. Crawford

Bibliographic record

VenueJournal of Pediatric Hematology/Oncology · 2017
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsPediatric Oncology Group
Fundersnot available
KeywordsMedulloblastomaMedicineRadiation therapyRegimenPediatricsRadiologySurgeryPathology

Abstract

fetched live from OpenAlex

Treatment for medulloblastoma carries significant risks, particularly in resource-constrained settings. We report a case of a Mexican infant with desmoplastic/nodular medulloblastoma. Given the nature of her tumor, we developed a tailored regimen following subtotal resection to avoid both radiation therapy and the high-dose cisplatin therapy offered at most centers in the United States. The patient is in remission 4 years after the initial diagnosis. This case suggests an alternative treatment plan for this particular tumor variant that accommodates the limited resources of many centers around the world and avoids the risks associated with radiation therapy at a young age.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.019
GPT teacher head0.312
Teacher spread0.294 · 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 designCase report
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

Citations5
Published2017
Admission routes1
Has abstractyes

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