MétaCan
Menu
Back to cohort

SWOG S1823/CCTG GCC1: Translational observational investigational study of the liquid biomarker microRNA 371a-3p in newly diagnosed germ cell tumours—Real-world trial design, rapid accrual, and robust secondary use of data opportunities.

2023· article· en· W4379282977 on OpenAlexaffabout
Antoine Morin Coulombe, Güliz Özgün, Rebecca H. Johnson, Mark A. Lewis, Nabil Adra, Scott E. Eggener, Bruce J. Roth, Christopher W. Ryan, Christopher R. Porter, Fred Millard, Thomas L. Jang, Robert J. Hamilton, Lawrence H. Einhorn, Christian Kollmannsberger, Kathryn B. Arnold, Charles D. Blanke, Siamak Daneshmand, Craig R. Nichols, Lucia Nappi, Andrea Harzstark

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsUniversity of British ColumbiaUniversity Health Network
Fundersnot available
KeywordsMedicineObservational studyInterim analysisBiomarkerOncologyInterimInternal medicineClinical trialSeminomaChemotherapy

Abstract

fetched live from OpenAlex

TPS5103 Background: With the discovery of the very promising, germ cell malignancy (GCM) specific, liquid biomarker microRNA 371a-3p (miR371), the investigative trajectory has markedly accelerated. With its outstanding, previously-reported specificity and positive predictive value, miR371 likely will become a powerful tool for clinical decision-making. The primary objective of SWOG S1823/CCTG GCC1 [NCT03067181] is to define the operating characteristics of plasma miR371 expression at the time of clinical relapse for low/moderate risk non-seminoma GCM patients on active surveillance. Methods: S1823 is a prospective, observational, adult GCM translational trial which is actively accruing. Broad eligibility includes all newly diagnosed adult GCM patients. Patients are assigned to low (<25% risk of relapse), moderate (25-90% risk of relapse) or high (≥90% risk of harboring active GCM) risk groups. Pragmatic logistics include using Streck tubes and centralized processing for miR371 sample processing and analytics. Research samples were drawn at the time of routine blood draws minimizing patient burden. Source documents are submitted along with case report forms. This data-gathering model gives an important data quality-control check. S1823 began in July 2020 and has since accelerated its accrual to consistently predicted new enrollments of 20-30 cases/month. As of January 2023, the study enrolled 389 low-risk, 114 moderate-risk and 146 high-risk patients (657 total). Interim analysis is planned at the time 40 non-seminoma GCM patients have relapsed and is anticipated in late 2023 to early 2024. 404 centers have opened S1823. 3 Canadian centers have enrolled >10 patients (range 11-51). 17 USA institutions have enrolled >10 patients (range 11-112). In North America, 9 of the 12-storied GCM clinical research programs have had robust participation. The leading accruing organization was the Kaiser Permanente system where 112 patients have been enrolled. In Canada, all population centers contributed proportionally. In the USA, the dominant enrollment comes from the west coast and mid-west. Proportional enrollments have been seen in Hispanic and Asian populations. The entire study provides rich opportunities for a variety of secondary use and patterns of care projects that will begin to roll out over the next year. Clinical trial information: NCT03067181 . [Table: see text]

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)0.010
metaresearch head score (Gemma)0.008
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 score0.181
Threshold uncertainty score0.917

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.589
GPT teacher head0.481
Teacher spread0.108 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Clinical OncologySame topicRadiomics and Machine Learning in Medical ImagingFrench-language works237,207