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Record W3159545343 · doi:10.2217/fon-2021-0153

Real-World Genetic Testing Patterns in Metastatic Castration-Resistant Prostate Cancer

2021· article· en· W3159545343 on OpenAlex
Neal D. Shore, Raluca Ionescu‐Ittu, Lingfeng Yang, François Laliberté, Malena Mahendran, Dominique Lejeune, Louise Yu, Joseph E. Burgents, Mei Sheng Duh, Sameer R. Ghate

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 · 2021
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsGroup for Research in Decision Analysis
FundersMerck Sharp and Dohme
KeywordsMedicineFANCAPALB2Prostate cancerOncologyGenetic testingInternal medicineCancerDNA repairGeneMutationFanconi anemiaGeneticsGermline mutation

Abstract

fetched live from OpenAlex

Aim: To assess the patterns of genetic testing for homologous recombination repair mutations in patients with metastatic castration-resistant prostate cancer (mCRPC) pre-PARP inhibitors approval. Patients & methods: mCRPC patients were selected in an oncology electronic medical records database. Patterns and predictors of testing for ATM, BRCA1/2, CDK12, PALB2 and FANCA gene alterations were assessed. Results: Of 5213 mCRPC patients, 674 (13%) had a documented genetic test. The number of tested patients increased from 1 in 2013 to 313 in 2018 (out of 3161 and 3010 clinically active patients, respectively). Receiving care in an academic oncology center (versus a community-based center) strongly predicted genetic testing (hazard ratio = 2.41). Conclusion: The use of and access to genetic testing pre-PARP inhibitor approval was suboptimal.

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)0.000
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 score0.220
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.056
GPT teacher head0.389
Teacher spread0.333 · 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