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Record W3021554968 · doi:10.51893/2020.2.ed2

Opportunities and challenges of clustering, crossing over, and using registry data in the PEPTIC trial

2020· article· en· W3021554968 on OpenAlexaff
Paul J. Young, Sean M. Bagshaw, Andrew Forbes, Alistair Nichol, Stephen E. Wright, Rinaldo Bellomo, Frank van Haren, Edward Litton, Steve Webb

Bibliographic record

VenueCritical Care and Resuscitation · 2020
Typearticle
Languageen
FieldMedicine
TopicAntibiotics Pharmacokinetics and Efficacy
Canadian institutionsUniversity of Alberta Hospital
Fundersnot available
KeywordsMedicineCluster analysisPepticData miningPeptic ulcerInternal medicineArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

The Proton Pump Inhibitors (PPIs) versus Histamine-2 Receptor Blockers (H2RBs) for Ulcer Prophylaxis Therapy in the Intensive Care Unit (ICU) (PEPTIC) trial is the largest randomised clinical trial ever conducted in the field of intensive care medicine. The potential clinical implications of the trial have been the subject of a previous editorial. Here we focus on the implications of the study for clinical trial science and on the opportunities the study provides for exploratory analyses that will potentially shed further light on the relative safety and efficacy of using PPIs or H2RBs for stress ulcer prophylaxis in the critically ill.

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)0.826
metaresearch head score (Gemma)0.900
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.826
Threshold uncertainty score0.215

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.8260.900
Meta-epidemiology (narrow)0.0030.004
Meta-epidemiology (broad)0.0140.010
Bibliometrics0.0090.015
Science and technology studies0.0070.016
Scholarly communication0.0230.024
Open science0.0130.015
Research integrity0.0210.025
Insufficient payload (model declined to judge)0.0060.002

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 head0.321
GPT teacher head0.420
Teacher spread0.099 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
Domainnot available
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
Published2020
Admission routes1
Has abstractyes

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