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Record W2527576682 · doi:10.1016/j.ijrobp.2016.06.304

Clinicians Underreport Adverse Events in NRG Oncology's Radiation Therapy Oncology Group 1203: The Importance of Using Patient-Reported Outcomes in Oncology Clinical Trials

2016· article· en· W2527576682 on OpenAlexaff
Anamaria R. Yeung, Stephanie L. Pugh, Ann H. Klopp, Krzysztof Gil, Lari Wenzel, Shannon N. Westin, David K. Gaffney, William Small, Spencer Thompson, Desiree E. Doncals, Guilherme Cantuaria, Brian Yaremko, Anne Lynn S. Chang, Vijayananda Kundapur, D.S. Mohan, Michael L. Haas, Yong Bae Kim, C.L. Ferguson, Lisa A. Kachnic

Bibliographic record

VenueInternational Journal of Radiation Oncology*Biology*Physics · 2016
Typearticle
Languageen
FieldMedicine
TopicAdvances in Oncology and Radiotherapy
Canadian institutionsCancer Care Ontario
Fundersnot available
KeywordsMedicineCommon Terminology Criteria for Adverse EventsInternal medicineToxicityAdverse effectGynecologic oncologyClinical trialMucositisOncologyRadiation therapy

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.286
metaresearch head score (Gemma)0.495
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.997
Threshold uncertainty score0.881

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2860.495
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.004
Science and technology studies0.0010.002
Scholarly communication0.0060.005
Open science0.0020.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0020.001

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 head0.114
GPT teacher head0.509
Teacher spread0.396 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designObservational
DomainReporting
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

Citations0
Published2016
Admission routes1
Has abstractno

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