Evidence map›Paper›PMID 36412645›Full record

ArticleVision (Basel, Switzerland)2022

Assessing the Performance of a Novel Bayesian Algorithm at Point of Care for Red Eye Complaints.

Alexander M Deans, Amy Basilious, Cindy M Hutnik

Open access · goldAbstract read
In one paragraph

Article in Vision (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 80% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

3 authors at 1 institution in 1 country.

Alexander M DeansSchulich School of Medicine and Dentistry, Western University, 1151 Richmond St., London, ON N6A 5C1, Canada.ORCID 0000-0002-3022-6233
Amy BasiliousSchulich School of Medicine and Dentistry, Western University, 1151 Richmond St., London, ON N6A 5C1, Canada.ORCID 0000-0003-4122-5321
Cindy M HutnikDepartment of Ophthalmology, Schulich School of Medicine and Dentistry, Western University, 1151 Richmond St., London, ON N6A 5C1, Canada.
Western University · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The current diagnostic aids for red eye are static flowcharts that do not provide dynamic, stepwise workups. The diagnostic accuracy of a novel dynamic Bayesian algorithm for red eye was tested. Fifty-seven patients with red eye were evaluated by an emergency medicine physician who completed a questionnaire about symptoms/findings (without requiring extensive slit lamp findings). An ophthalmologist then attributed an independent "gold-standard diagnosis". The algorithm used questionnaire data to suggest a differential diagnosis. The referrer's diagnostic accuracy was 70.2%, while the algorithm's accuracy was 68.4%, increasing to 75.4% with the algorithm's top two diagnoses included and 80.7% with the top three included. In urgent cases of red eye (

Indexed as

algorithmdiagnosisdifferentialred eye

Identifiers

PMID36412645
PMCPMC9680424
OpenAlexW4307187990

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.