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Low Incidence of Ototoxicity With Continuous Infusion of Cisplatin in the Treatment of Pediatric Germ Cell Tumors

2006· article· en· W2036442240 on OpenAlexaff
Abha A. Gupta, Michael Capra, Vicky Papaioannou, Gregg Hall, Ronnen Maze, David Dix, Sheila Weitzman

Bibliographic record

VenueJournal of Pediatric Hematology/Oncology · 2006
Typearticle
Languageen
FieldMedicine
TopicTesticular diseases and treatments
Canadian institutionsBritish Columbia Children's HospitalHospital for Sick Children
Fundersnot available
KeywordsMedicineOtotoxicityCisplatinIncidence (geometry)Germ cell tumorsGerm cellOncologyChemotherapyInternal medicineGenetics

Abstract

fetched live from OpenAlex

Cisplatin is an important chemotherapeutic agent in the treatment of many pediatric malignancies, but its use is limited in part by ototoxicity. The authors' institution has been administering standard-dose cisplatin by continuous infusion rather than bolus administration in germ cell tumors. The authors retrospectively reviewed 39 patients with germ cell tumors requiring chemotherapy over the past 20 years and recorded data including demographics, cumulative cisplatin dose, degree of ototoxicity (by the Brock grading system), and disease outcome. The median age was 9.4 years and the majority of children (48.7%) had endodermal sinus tumor. Twenty-one children received 400 mg/m of cisplatin or more. One child had evidence of significant ototoxicity at last follow-up (6.64 years after diagnosis). This patient received a total cumulative dose of 500 mg/m of cisplatin. Eighty-two percent of children achieved clinical remission of their disease. The authors conclude that continuous administration of cisplatin is associated with minimal ototoxicity while maintaining good tumoricidal efficacy, and further studies using this regimen are warranted.

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)0.006
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.277
Teacher spread0.268 · 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

Citations27
Published2006
Admission routes1
Has abstractyes

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