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Record W2803082375 · doi:10.1016/j.ijrobp.2018.04.030

Failed Randomized Clinical Trials in Radiation Oncology: What Can We Learn?

2018· article· en· W2803082375 on OpenAlexaff
Timothy K. Nguyen, Eric K. Nguyen, Andrew Warner, Alexander V. Louie, David A. Palma

Bibliographic record

VenueInternational Journal of Radiation Oncology*Biology*Physics · 2018
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsWestern UniversityJuravinski Cancer CentreLondon Health Sciences Centre
Fundersnot available
KeywordsMedicineClinical trialRandomized controlled trialInterim analysisInternal medicineOdds ratioLogistic regressionMultivariate analysis

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)0.547
metaresearch head score (Gemma)0.784
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.453
Threshold uncertainty score0.558

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.5470.784
Meta-epidemiology (narrow)0.0050.006
Meta-epidemiology (broad)0.0350.022
Bibliometrics0.0070.010
Science and technology studies0.0030.011
Scholarly communication0.0200.033
Open science0.0130.008
Research integrity0.0240.018
Insufficient payload (model declined to judge)0.0080.001

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 head0.514
GPT teacher head0.580
Teacher spread0.065 · 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
DomainMethods
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

Citations53
Published2018
Admission routes1
Has abstractno

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