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Record W4288518871 · doi:10.14740/jocmr4764

Carbapenemase Inhibitors: Updates on Developments in 2021

2022· review· en· W4288518871 on OpenAlexvenueno aff
Maroun Bou Zerdan, Sally Al Hassan, Waleed Shaker, Rayan El Hajjar, Sabine Allam, Morgan Bou Zerdan, Amal Naji, Nabil Zeineddine

Bibliographic record

VenueJournal of Clinical Medicine Research · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAvibactamIntensive care medicineBroad spectrumAntimicrobialCeftazidime/avibactamAntibiotic resistanceClinical trialAntibioticsMicrobiologyInternal medicinePseudomonas aeruginosaBiologyBacteria

Abstract

fetched live from OpenAlex

Carbapenem resistance, an emerging global health problem, compromises the treatment of infections caused by nosocomial pathogens. Preclinical and clinical trials demonstrate that a new generation of carbapenemases inhibitors, together with the recently approved avibactam, relebactam and vaborbactam, would address this resistance. Our review summarizes the latest developments related to carbapenemase inhibitors synthesized to date, as well as their spectrum of activity and their current stage of development. A particular focus will be on β-lactam/β-lactamase inhibitor combinations that could potentially be used to treat infections caused by carbapenemase-producer pathogens. These new combinations mark a critical step forward the fight against antimicrobial resistance.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.005

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 head0.313
GPT teacher head0.574
Teacher spread0.262 · 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 designNot applicable
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

Citations12
Published2022
Admission routes1
Has abstractyes

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