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Record W2905463162 · doi:10.1001/jamaoncol.2018.5877

Assessment of Frequency and Reporting of Changes in Cancer Trial Design After Initiation of Patient Accrual

2018· letter· en· W2905463162 on OpenAlexaff
Daniel Shepshelovich, Ariadna Tibau, Consolación Moltó, Hadar Goldvaser, Alberto Ocaña, Boštjan Šeruga, Eitan Amir

Bibliographic record

VenueJAMA Oncology · 2018
Typeletter
Languageen
FieldMathematics
TopicStatistical Methods in Clinical Trials
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer Centre
FundersVetenskapsrådetNational Natural Science Foundation of China
KeywordsMedicineAccrualClinical trialResearch designClinical study designOncologyDrug trialInternal medicineStatisticsAccounting

Abstract

fetched live from OpenAlex

This study matches entries in ClinicalTrials.gov for cancer drug trials at initiation of the trial with later entries and with journal reports of the trials to assess modifications in trial design, external factors associated with such modifications, and transparency in reporting them.

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 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)0.385
metaresearch head score (Gemma)0.834
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.615
Threshold uncertainty score0.759

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3850.834
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.009
Science and technology studies0.0020.002
Scholarly communication0.0050.005
Open science0.0020.003
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0040.002

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 head0.665
GPT teacher head0.617
Teacher spread0.049 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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