Evidence map›Paper›PMID 35938054›Full record

ArticleThe British and Irish orthoptic journal2022

Using a Computerised Staircase and Incremental Optotype Sizes to Improve Visual Acuity Assessment Accuracy.

Anna O'Connor, Chloe King, Ashli Milling, Laurence Tidbury

Erratum issuedAbstract read
In one paragraph

Article in The British and Irish orthoptic journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Anna O'ConnorUniversity of Liverpool, GB.ORCID https://orcid.org/0000-0002-0376-9670
Chloe KingUniversity of Liverpool, GB.ORCID https://orcid.org/0000-0002-0295-4903
Ashli MillingUniversity of Liverpool, GB.ORCID https://orcid.org/0000-0002-4984-3469
Laurence TidburyUniversity of Liverpool, GB.ORCID https://orcid.org/0000-0002-7748-8791

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Given the impact of visual acuity results on diagnosis and management, it is essential that the test is accurate, determined by factors such as test-retest variability. Standardisation improves accuracy, which can be performed via a computerised staircase methodology. Standard clinical tests with scoring of 0.02 per optotype implies an incremental score per optotype despite optotype size remaining constant on each line. The aim of this study is to establish if near continuous incremental optotype display and scoring improves test-retest variability compared to current testing methods. Methods: A computerised three up, one down adaptive staircase was used to display Kay Picture optotypes on an LCD monitor. Three methods of visual acuity assessment were undertaken: ETDRS, Kay Pictures and computerised Kay Pictures. Tests were performed twice under standard clinical conditions. Results: One hundred nineteen adults were tested. Test-retest variability for computerised Kay pictures was 0.01 logMAR (±0.04, p = 0.001). Good levels of agreement were observed for computerised Kay pictures in terms of test-retest variability, where the test had the smallest mean bias (0.01 logMAR compared to 0.03 and 0.08 logMAR for Kay Pictures and ETDRS respectively) and narrowest limits of agreement. Participants performed better in computerised Kay pictures than Kay Pictures by 0.03 logMAR, and better in ETDRS than computerised Kay pictures by 0.1 logMAR. Conclusion: Computerised Kay pictures exhibited a low test-retest variability, demonstrating it is reliable and repeatable. This repeatability measure is lower than the test-retest variability of the ETDRS and Kay Pictures tests.

Indexed as

test-retest variabilityvision testsVisual acuity

Identifiers

PMID35938054
PMCPMC9306677

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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.