Evidence map›Paper›PMID 33719780›Full record

ArticleJournal of veterinary diagnostic investigation : official publication of the American Association of Veterinary Laboratory Diagnosticians, Inc2021

Disease diagnostic coding to facilitate evidence-based medicine: current and future perspectives.

Rachel J Derscheid, Michael C Rahe, Eric R Burrough, Kent J Schwartz, Bailey Arruda

Open access · bronzeAbstract read
In one paragraph

Article in Journal of veterinary diagnostic investigation : official publication of the American Association of Veterinary Laboratory Diagnosticians, Inc, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
1.7field-weighted citation impact, top 17% 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

12 citing papers in PubMed, 17 citations in OpenAlex.

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  7. Assessing the detection and interaction ofFrontiers in veterinary science · 2024
    Article
  8. Article
  9. Article
  10. Visualization and application of disease diagnosis codes for population health management using porcine diseases as a model.Journal of veterinary diagnostic investigation : official publication of the American Association of Veterinary Laboratory Diagnosticians, Inc · 2021
    Article
  11. Leveraging and enhancing the value of veterinary diagnostic laboratory data.Journal of veterinary diagnostic investigation : official publication of the American Association of Veterinary Laboratory Diagnosticians, Inc · 2021
    Article
  12. Data standardization implementation and applications within and among diagnostic laboratories: integrating and monitoring enteric coronaviruses.Journal of veterinary diagnostic investigation : official publication of the American Association of Veterinary Laboratory Diagnosticians, Inc · 2021
    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

5 authors at 1 institution in 1 country.

Rachel J DerscheidVeterinary Diagnostic Laboratory, Iowa State University College of Veterinary Medicine, Ames, IA.ORCID https://orcid.org/0000-0002-4402-5862
Michael C RaheVeterinary Diagnostic Laboratory, Iowa State University College of Veterinary Medicine, Ames, IA.ORCID https://orcid.org/0000-0003-2123-4124
Eric R BurroughVeterinary Diagnostic Laboratory, Iowa State University College of Veterinary Medicine, Ames, IA.ORCID https://orcid.org/0000-0003-4747-9189
Kent J SchwartzVeterinary Diagnostic Laboratory, Iowa State University College of Veterinary Medicine, Ames, IA.
Bailey ArrudaVeterinary Diagnostic Laboratory, Iowa State University College of Veterinary Medicine, Ames, IA.
Iowa State University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Technologic advances in information management have rapidly changed laboratory testing and the practice of veterinary medicine. Timely and strategic sampling, same-day assays, and 24-h access to laboratory results allow for rapid implementation of intervention and treatment protocols. Although agent detection and monitoring systems have progressed, and wider tracking of diseases across veterinary diagnostic laboratories exists, such as by the National Animal Health Laboratory Network (NAHLN), the distinction between detection of agent and manifestation of disease is critical to improved disease management. The implementation of a consistent, intuitive, and useful disease diagnosis coding system, specific for veterinary medicine and applicable to multiple animal species within and between veterinary diagnostic laboratories, is the first phase of disease data aggregation. Feedback loops for continuous improvement that could aggregate existing clinical and laboratory databases to improve the value and applications of diagnostic processes and clinical interventions, with interactive capabilities between clinicians and diagnosticians, and that differentiate disease causation from mere agent detection, remain incomplete. Creating an interface that allows aggregation of existing data from clinicians, including final diagnosis, interventions, or treatments applied, and measures of outcomes, is the second phase. Prototypes for stakeholder cooperation, collaboration, and beta testing of this vision are in development and becoming a reality. We focus here on how such a system is being developed and utilized at the Iowa State University Veterinary Diagnostic Laboratory to facilitate evidence-based medicine and utilize diagnostic coding for continuous improvement of animal health and welfare.

Indexed as

Databases, FactualAnimal DiseasesAnimalsClinical CodingEvidence-Based MedicineIowaLaboratoriesVeterinary Medicinedata warehousediagnosisdiagnostic codesdiagnostic coding

Identifiers

PMID33719780
PMCPMC8120077
OpenAlexW3134828485

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.