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Record W2061476279 · doi:10.1200/jco.2006.10.3036

Ethics in Oncology: Consulting for the Investment Industry

2007· article· en· W2061476279 on OpenAlexaff
Jordan Berlin, Suanna S. Bruinooge, Ian F. Tannock

Bibliographic record

VenueJournal of Clinical Oncology · 2007
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineInvestment (military)Clinical OncologyOncologyInternal medicineFamily medicineCancer

Abstract

fetched live from OpenAlex

As Ethics Committee Chair, I am pleased to introduce the first in an ongoing series of ethics vignettes. These columns, which are based on true-to-life situations that arise in oncology research and practice, are intended to identify and explore important ethical issues and provide commentary that is specific to oncology. Please look for them periodically in both the Journal of Clinical Oncology and the Journal of Oncology Practice. The idea for publishing vignettes evolved through the joint efforts of the Ethics Committee and the Board of Directors. Rather than adopt a single set of ethical principles that applies vaguely to any situation and well to none, the Committee and the Board preferred to tackle ethical dilemmas individually, specifically, and directly. Because the Ethics Committee thought the ethical and legal implications of physician interactions with the investment industry were so important and timely, it chose to address this topic in both a position article, which was previously published in the January 20, 2007, issue of the Journal of Clinical Oncology (J Clin Oncol 25:338-340, 2007) and in its first vignette column. The Ethics Committee hopes this column will be the first of several that ASCO members will find helpful as they grapple with the many ethical issues that arise in daily practice in the field of oncology. Because these columns are intended to address the concerns of ASCO members, the Committee welcomes suggestions for future topics at vignettes@asco.org. Martin D. Abeloff, MD, Chair, Ethics Committee.

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)0.039
metaresearch head score (Gemma)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesMetaresearch, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.944
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0390.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0030.007
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 head0.418
GPT teacher head0.600
Teacher spread0.182 · 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; both teacher heads agree on what is shown here.

Study designOther design
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

Citations10
Published2007
Admission routes1
Has abstractyes

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