Evidence map›Paper›PMID 22253798›Full record

ArticlePloS one2012

"One-size-fits-all"? Optimizing treatment duration for bacterial infections.

Patricia Geli, Ramanan Laxminarayan, Michael Dunne, David L Smith

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed
4.8field-weighted citation impact, top 5% of its field
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

37 citing papers in PubMed, 75 citations in OpenAlex.

  1. Article
  2. Article
  3. Alternate Antimicrobial Therapies and Their Companion Tests.Diagnostics (Basel, Switzerland) · 2023
    Review
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  14. Review
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  16. An ecosystem framework for understanding and treating disease.Evolution, medicine, and public health · 2018
    Review
  17. Review
  18. Article
  19. Article
  20. Article
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

4 authors at 1 institution in 1 country.

Patricia GeliCenter for Disease Dynamics, Economics and Policy, Washington, D.C., United States of America.
Ramanan Laxminarayan
Michael Dunne
David L Smith
Center for Disease Dynamics, Economics & Policy · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Historically, antibiotic treatment guidelines have aimed to maximize treatment efficacy and minimize toxicity, but have not considered the evolution of antibiotic resistance. Optimizing the duration and dosing of treatment to minimize the duration of symptomatic infection and selection pressure for resistance simultaneously has the potential to extend the useful therapeutic life of these valuable life-saving drugs without compromising the interests of individual patients.Here, using mathematical models, we explore the theoretical basis for shorter durations of treatment courses, including a range of ecological dynamics of bacteria that cause infections or colonize hosts as commensals. We find that immunity is an important mediating factor in determining the need for long duration of treatment. When immunity to infection is expected, shorter durations that reduce the selection for resistance without interfering with successful clinical outcome are likely to be supported. Adjusting drug treatment strategies to account for the impact of the differences in the ecological niche occupied by commensal flora relative to invasive bacteria could be effective in delaying the spread of bacterial resistance.

Indexed as

AnimalsAnti-Bacterial AgentsBacteriaBacterial InfectionsColony Count, MicrobialComputer SimulationDrug Resistance, BacterialHumansTime FactorsTreatment OutcomeAnti-Bacterial Agents

Identifiers

PMID22253798
PMCPMC3256207
OpenAlexW2028753705

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.