Evidence map›Paper›PMID 35815912›Full record

ArticleJournal of veterinary internal medicine2022

Googling for a veterinary diagnosis: A replication study using Google as a diagnostic aid.

E Carley Allen, Kristine M Alpi, George W Schaaf, Steven L Marks

Abstract read
In one paragraph

Article in Journal of veterinary internal medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

E Carley AllenRed Bank Veterinary Hospitals, Tinton Falls, New Jersey, USA.ORCID https://orcid.org/0000-0001-7169-5215
Kristine M AlpiCollege of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina, USA.ORCID https://orcid.org/0000-0002-4521-3523
George W SchaafWake Forest University School of Medicine, Winston Salem, North Carolina, USA.ORCID https://orcid.org/0000-0001-7129-9474
Steven L MarksCollege of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina, USA.ORCID https://orcid.org/0000-0003-3977-8875

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe purpose of this study was to replicate in the veterinary context a BMJ study using Google to assist in diagnosis of complex cases. HYPOTHESIS/

objectivesTo assess percentage of diagnoses identified using Google as a diagnostic aid in veterinary medicine. ANIMALS: None; 13 cases in cats and 17 in dogs published in JAVMA.

methodsCross-sectional survey of Google results from searches using keywords generated independently by a generalist and a specialist veterinarian who reviewed the published case history and diagnostic components while blind to the diagnosis. They offered diagnoses and generated up to 5 search strategies for each case. The top 30 Google results for each search were reviewed by the generalist to inform a final Google-aided diagnosis. Both veterinarians' initial diagnoses and the Google-aided diagnoses were compared with the published diagnoses.

resultsGoogle searching led to 52 diagnoses out of 60 possible. Twenty-two (42%, 95% confidence interval [95% CI] 29%-55%) Google-aided diagnoses matched the JAVMA diagnosis. This accuracy rate does not differ significantly from 58% (n = 15/26, 95% CI 38%-77%) identified in the BMJ study. Google-aided results were not statistically different from those achieved unaided by each veterinarian (33%, 95% CI 16%-50%). CONCLUSIONS AND CLINICAL IMPORTANCE: Published information found searching Google using keywords related to complicated or unusual cases could assist veterinarians to reinforce their initial diagnosis or consider other differential diagnoses. Search strategies using words representing either signs or the preliminary diagnoses can yield results useful to confirming a correct diagnosis.

Indexed as

Search EngineVeterinariansAnimalsCat DiseasesCatsCross-Sectional StudiesDiagnosis, DifferentialDog DiseasesDogsVeterinary Medicinedifferential diagnosesinformation resourcesopen accessproblem lists

Identifiers

PMID35815912
PMCPMC9308411

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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.