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Record W3205679927 · doi:10.1128/aac.01627-21

Secondary Analysis of a Systematic Review: Are Antifungal Noninferiority Trials at Risk of Eroding Effectiveness Because of Biocreep?

2022· article· en· W3205679927 on OpenAlexaff
Adam S. Komorowski, Anthony D. Bai, Anna Cvetkovic, Omar Mourad, Carson K. L. Lo, Xena X. Li, Vaibhav Mokashi, Aidan Findlater, Donald Brody Duncan, Daniela Leto, Deborah Yamamura, Dominik Mertz

Bibliographic record

VenuePubMed · 2022
Typearticle
Languageen
FieldMathematics
TopicStatistical Methods in Clinical Trials
Canadian institutionsMcMaster UniversityQueen's UniversityMcMaster University Medical Centre
Fundersnot available
KeywordsRandomized controlled trialAntifungalMeta-analysisMedicineSample size determinationRisk analysis (engineering)Internal medicineStatisticsMathematics

Abstract

fetched live from OpenAlex

Noninferiority randomized controlled trial (RCT) effectiveness may erode when results favor the active control over time and when a decreasingly effective control arm is used in serial trials. We analyzed 32 antifungal noninferiority RCTs (NI-RCTs) for these scenarios in this secondary analysis of a systematic review. Our exploratory analysis suggests that the erosion risk in the effectiveness of antifungal noninferiority trials is uncommon. Findings are limited by small sample size and overall risk of bias.

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)0.468
metaresearch head score (Gemma)0.774
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.532
Threshold uncertainty score0.657

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4680.774
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0110.029
Bibliometrics0.0150.015
Science and technology studies0.0010.005
Scholarly communication0.0080.007
Open science0.0040.007
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0140.002

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 head0.490
GPT teacher head0.503
Teacher spread0.013 · 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 designSystematic review
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

Citations1
Published2022
Admission routes1
Has abstractyes

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