Evidence map›Paper›PMID 31392582›Full record

ReviewInternational journal of clinical pharmacy2019

Optimising antimicrobial therapy through the use of Bayesian dosing programs.

M L Avent, B A Rogers

Abstract readReview
PubMed Publisher
In one paragraph

Review in International journal of clinical pharmacy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

M L AventInfection and Immunity Theme, UQ Centre for Clinical Research (UQCCR), The University of Queensland, Level 5, Building 71/918 Royal Brisbane Hospital, Herston, QLD, 4006, Australia. m.avent@uq.edu.au.ORCID http://orcid.org/0000-0001-7228-2834
B A RogersCentre for Inflammatory Diseases, Monash University, Clayton, VIC, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The optimisation of antibiotic dosing therapy with therapeutic drug monitoring is widely recommended. The aim of therapeutic drug monitoring is to help the clinician to achieve target pharmacokinetic/pharmacodynamic parameters, maximising efficacy and minimising toxicity. Computerised programs, utilising the Bayesian estimation procedures, are able to achieve target concentrations in a greater percentage of patients earlier in the course of therapy compared to linear regression analysis and population methods. This article summarises various methods for dose optimisation of antibiotics with a focus on Bayesian programs.

Indexed as

Bayes TheoremAnti-Bacterial AgentsAnti-Infective AgentsDrug MonitoringHumansAnti-Bacterial AgentsAnti-Infective AgentsAntibioticsBayesian statisticsPharmacodynamicsPharmacokineticsSoftware

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.