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Record W3082857472 · doi:10.1128/aac.01597-20

Novel Antibiotics May Be Noninferior but Are They Becoming Less Effective?: a Systematic Review

2020· review· en· W3082857472 on OpenAlexaff
Anthony D. Bai, Adam S. Komorowski, Carson K. L. Lo, Pranav Tandon, Xena X. Li, Vaibhav Mokashi, Anna Cvetkovic, Aidan Findlater, Laurel Liang, Mark Loeb, Dominik Mertz

Bibliographic record

VenueAntimicrobial Agents and Chemotherapy · 2020
Typereview
Languageen
FieldMathematics
TopicStatistical Methods in Clinical Trials
Canadian institutionsMcMaster University Medical CentreUniversity of TorontoMcMaster University
Fundersnot available
KeywordsAntibioticsMedicineIntensive care medicineSystematic reviewClinical trialMEDLINEInternal medicineBiologyMicrobiology

Abstract

fetched live from OpenAlex

Novel antibiotics approved by noninferiority trials may become less effective over time in two scenarios: (i) the treatment effect in studies of novel antibiotics may be consistently worse than studies of older antibiotics; (ii) when a decreasingly effective control arm is used in a series of noninferiority trials. Our systematic review of 175 noninferiority antibiotic trials found these scenarios to be rare.

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)0.020
metaresearch head score (Gemma)0.091
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.020
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.091
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.006
Bibliometrics0.0050.005
Science and technology studies0.0000.001
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.409
GPT teacher head0.513
Teacher spread0.104 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
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

Citations9
Published2020
Admission routes1
Has abstractyes

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