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Record W2296581273 · doi:10.21037/tau.2016.02.01

Predicting response to neoadjuvant chemotherapy in bladder cancer: controversies remain with genomic DNA sequencing

2016· article· en· W2296581273 on OpenAlexaff
Roland Seiler

Bibliographic record

VenueTranslational Andrology and Urology · 2016
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBladder cancerCisplatinChemotherapyMedicineComplete responseOncologyPathologicalDNA sequencingBiomarkerNeoadjuvant therapyInternal medicineCancerBioinformaticsDNABiologyGenetics

Abstract

fetched live from OpenAlex

Neoadjuvant cisplatin-based chemotherapy (NAC) in muscle-invasive bladder cancer is an accepted standard of care (1,2). NAC improves patient outcomes quantified by a 5–8% higher 5-year overall survival (OS) and an increase of pathological downstaging of 10–15% (3-5). However, a considerable number of patients do not response to NAC. They are over treated and suffer from unnecessary adverse effects. This led biomarker researchers focus on the prediction of response to NAC (6-11), including the recently published study carried out by Plimack et al ., which performed genomic DNA sequencing of pretreatment tumor tissue (12).

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)0.000
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 score0.135
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.012
GPT teacher head0.256
Teacher spread0.244 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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