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Record W3199829819 · doi:10.1002/pbc.29365

Prognostic factors for patients with relapsed central nervous system nongerminomatous germ cell tumors

2021· review· en· W3199829819 on OpenAlexaff
Mohammad H Abu-Arja, Diana S. Osorio, Álvaro Lassaletta, Richard Graham, Scott Coven, Joseph Stanek, Éric Bouffet, Jonathan L. Finlay, Mohamed S Abdelbaki

Bibliographic record

VenuePediatric Blood & Cancer · 2021
Typereview
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsHospital for Sick Children
FundersAgency for Healthcare Research and Quality
KeywordsMedicineCentral nervous systemRadiation therapyCerebrospinal fluidInternal medicineGerm cell tumorsChemotherapyBone marrowGastroenterologyOncologySurgery

Abstract

fetched live from OpenAlex

We aimed toidentify prognostic factors that may help better understand the behavior of relapsed central nervous system nongerminomatous germ cell tumors. We identified nine studies, including 101 patients; 33 patients (33%) were alive 12 months post-initial relapse. Sixty percent of patients with serum/cerebrospinal fluid (CSF) alpha-fetoprotein (AFP) level ≤25 ng/mL at initial diagnosis were survivors compared with 28% among patients with serum/CSF AFP level >25 ng/mL (P = 0.01). Seventy-one percent of patients who achieved complete response/continued complete response (CR/CCR) by the end of therapy at relapse were survivors compared with 7% among patients who had less than CR/CCR (P < 0.0001). Forty-eight percent of patients who received marrow-ablative chemotherapy followed by autologous hematopoietic cell rescue (HDCx/AuHCR) following relapse were survivors compared with 12% among patients who did not receive HDCx/AuHCR (P = 0.0001). Local relapse site, gross total surgical resection, and radiotherapy at relapse were not associated with improved outcomes.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.017
GPT teacher head0.265
Teacher spread0.247 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations6
Published2021
Admission routes1
Has abstractyes

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