MétaCan
Menu
Back to cohort
Record W2955934677 · doi:10.2217/fon-2019-0010

Utilization of Tumor Genomics in Clinical Practice: An International Survey among ASCO Members

2019· article· en· W2955934677 on OpenAlex
Romualdo Barroso‐Sousa, Hao Guo, Piyush Srivastava, Ted A. James, Walter Birch, Lillian L. Siu, William P. Tew, Sara M. Tolaney

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFuture Oncology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsPrincess Margaret Cancer Centre
FundersAmerican Society of Clinical Oncology
KeywordsMedicineContext (archaeology)Family medicineClinical PracticeClinical trialClinical OncologyPrecision medicinePersonalized medicineInternal medicineCancerPathologyBioinformatics

Abstract

fetched live from OpenAlex

Aim: To identify patterns of use and barriers to tumor genomic testing among oncologists. Methods: We surveyed American Society of Clinical Oncology physician members about their use of genomic testing. Results: Among 11,900 members surveyed, a total of 1000 responded to the survey (participation rate, 8.4%). A total of 75% of the respondents included in the analysis reported ordering tests for at least 1–10% of their patients. Practice setting (academic vs community) was only a determinant in the ordering frequency in North America. Regardless of location, academic oncologists were more likely to prescribe medicine in the context of a clinical trial. Access to clinical trials and costs associated with testing were the barriers identified worldwide. Conclusion: There is substantial variation in the use of genomic tools according to region and practice setting; yet, the barriers are similar worldwide.

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.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.373
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.025
GPT teacher head0.373
Teacher spread0.347 · 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