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Record W2914598381 · doi:10.3899/jrheum.181054

Adaptive Trial Designs in Rheumatology: Report from the OMERACT Special Interest Group

2019· review· en· W2914598381 on OpenAlexaffvenue
Timothy Pickles, Rieke Alten, Maarten Boers, Vivian P. Bykerk, Jared Christensen, Robin Christensen, Hubert van Hoogstraten, Lee S. Simon, Lai‐Shan Tam, Ernest Choy

Bibliographic record

VenueThe Journal of Rheumatology · 2019
Typereview
Languageen
FieldMathematics
TopicStatistical Methods in Clinical Trials
Canadian institutionsUniversity of TorontoMount Sinai Hospital
FundersSanofiPfizerCelltrionEli Lilly and Company
KeywordsMedicineRheumatologyInternal medicinePhysical therapyFamily medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Adaptive trial design was developed initially for oncology to improve trial efficiency. If optimized for rheumatology, it may improve trial efficiency by reducing sample size and time. METHODS: A systematic review assessed design of phase II clinical trials in rheumatoid arthritis. RESULTS: Fifty-six trials were reviewed. Most trials had 4 groups (1 control and 3 intervention), with an average group size of 34 patients. American College of Rheumatology 20 measured at 16 weeks was the most commonly used primary endpoint. CONCLUSION: The next step is to undertake a systematic review of adaptive designs used in early-phase trials in nonrheumatic conditions.

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)0.020
metaresearch head score (Gemma)0.160
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.734
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.160
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0090.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0030.000
Research integrity0.0010.004
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 head0.749
GPT teacher head0.573
Teacher spread0.175 · 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; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations8
Published2019
Admission routes2
Has abstractyes

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