Evidence map›Paper›PMID 27510833›Full record

ArticleJournal of clinical microbiology2016

Automatic Digital Plate Reading for Surveillance Cultures.

Thomas J Kirn

Abstract readComment
In one paragraph

Article in Journal of clinical microbiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Automatic classification of fungal-fungal interactions using deep leaning models.Computational and structural biotechnology journal · 2024
    Article
  2. Article
  3. Image analysis and artificial intelligence in infectious disease diagnostics.Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases · 2020
    Review
  4. Impact of total laboratory automation on workflow and specimen processing time for culture of urine specimens.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2018
    Article
  5. Article
  6. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

1 author.

Thomas J KirnDepts. of Pathology & Laboratory Medicine and Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA KIRNTJ@Rutgers.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The automation of specimen processing and culture workup has rapidly emerged in clinical microbiology laboratories throughout the world and more recently in the United States. While many U.S. laboratories have implemented some form of automated specimen processing and some have begun performing digital plate reading, automated colony analysis is just beginning to be utilized clinically. In this issue of the Journal of Clinical Microbiology, M. L. Faron et al. (J Clin Microbiol 54:2470-2475, 2016, http://dx.doi.org/10.1128/JCM.01040-16) report the results of their evaluation of the performance of the WASPLab Chromogenic Detection Module (CDM) for categorizing chromogenic agar plates as negative or "nonnegative" for vancomycin-resistant enterococci (VRE). Their major finding was 100% sensitivity for detection of "nonnegative" specimens using CDM compared to manual methods for specimens plated on two different types of VRE chromogenic agar plates. Additionally, utilization of digital plate reading in conjunction with automated colony analysis was predicted to result in significant savings based on greatly reduced labor costs.

Indexed as

Image Interpretation, Computer-AssistedVancomycin ResistanceAgarAutomationEnterococcusHumansMicrobiological TechniquesSensitivity and SpecificityVancomycin-Resistant EnterococciAgar

Identifiers

PMID27510833
PMCPMC5035426

What OpenQuestion holds

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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.