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Record W2715795126 · doi:10.1183/13993003.00361-2017

Run-in bias in randomised trials: the case of COPD medications

2017· editorial· en· W2715795126 on OpenAlexaff
Samy Suissa

Bibliographic record

VenueEuropean Respiratory Journal · 2017
Typeeditorial
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsJewish General HospitalMcGill University
Fundersnot available
KeywordsMedicineCOPDIntensive care medicinePhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

Most randomised trials of newer and established medications in chronic obstructive pulmonary disease (COPD) enrol patients who have already been treated, often with drugs of the same class or even with the very same drugs that are to be studied. Some trials such as UPLIFT evaluated the effect of a drug as an add-on, with patients continuing their existing treatments [1]. Other trials, such as OPTIMAL and the Salford Lung Study, discontinued the regular maintenance treatment and replaced it with a randomly allocated active treatment [2, 3]. Finally, trials such as TORCH and SUMMIT had patients discontinue their regular maintenance treatment and directly receive the randomly allocated treatment, which included placebo [4, 5]. This last approach, however, has been shown to complicate the interpretation of results, particularly under the placebo group after the discontinuation of maintenance therapy [6, 7]. Randomised trials of COPD medications involving a “run-in” period could be biased: interpret results with caution

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)0.221
metaresearch head score (Gemma)0.551
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.779
Threshold uncertainty score0.961

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2210.551
Meta-epidemiology (narrow)0.0050.003
Meta-epidemiology (broad)0.0100.008
Bibliometrics0.0060.005
Science and technology studies0.0030.016
Scholarly communication0.0130.014
Open science0.0110.004
Research integrity0.0320.042
Insufficient payload (model declined to judge)0.0080.005

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 head0.095
GPT teacher head0.391
Teacher spread0.296 · 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 designNot applicable
DomainMethods
GenreEditorial

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

Citations33
Published2017
Admission routes1
Has abstractyes

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