Evidence map›Paper›PMID 33167775›Full record

ArticleActa orthopaedica2021

Are conventional microbiological diagnostics sufficiently expedient in the era of rapid diagnostics? Evaluation of conventional microbiological diagnostics of orthopedic implant-associated infections (OIAI).

Hege Vangstein Aamot, J Christopher Noone, Inge Skråmm, Truls M Leegaard

Open access · goldAbstract read
In one paragraph

Article in Acta orthopaedica, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.0field-weighted citation impact, top 22% 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

4 citing papers in PubMed, 7 citations in OpenAlex.

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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

4 authors at 2 institutions in 1 country.

Hege Vangstein AamotDepartment of Microbiology and Infection Control, Akershus University Hospital, Lørenskog.
J Christopher NooneDepartment of Microbiology and Infection Control, Akershus University Hospital, Lørenskog.
Inge SkråmmOrthopedic clinic, Akershus University Hospital, Lørenskog.
Truls M LeegaardDepartment of Microbiology and Infection Control, Akershus University Hospital, Lørenskog.
Akershus University Hospital · NOUniversity of Oslo · NO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and purpose - In a time when rapid diagnostics are increasingly sought, conventional procedures for detection of microbes causing orthopedic implant-associated infections (OIAI) seem extensive and time-consuming, but how extensive are they? We assessed time to (a) pathogen identification, (b) antibiotic susceptibility patterns, and (c) targeted antibiotic treatment using conventional microbiological diagnostics of OIAI in a consecutive series of patients.Patients and methods - Consecutive patients aged ≥18 years undergoing first revision surgery for acute OIAI, including prosthetic joints, fracture, and osteotomy implants, in 2017-2018 at Akershus University Hospital (Ahus), Norway were included. Information regarding microbiological diagnostics and clinical data was collected retrospectively from the hospital's diagnostic and clinical databases.Results - 123 patients fulfilled the inclusion criteria. Median time to pathogen identification was 2.5 days and to antibiotic treatment recommendations was 3.5 days. The most common pathogens were

Indexed as

Clinical Laboratory TechniquesAdultAgedAged, 80 and overAnti-Bacterial AgentsCohort StudiesFemaleHumansMaleMiddle AgedProstheses and ImplantsProsthesis-Related InfectionsReoperationRetrospective StudiesSpectrometry, Mass, Matrix-Assisted Laser Desorption-IonizationTime FactorsAnti-Bacterial Agents

Identifiers

PMID33167775
PMCPMC8158189
OpenAlexW3101137790

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Registered trials

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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.