Evidence map›Paper›PMID 33510958›Full record

ArticleTranslational vision science & technology2021

Improving Personalized Structure to Function Mapping From Optic Nerve Head to Visual Field.

Andrew Turpin, Allison M McKendrick

Abstract read
In one paragraph

Article in Translational vision science & technology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  7. Hierarchical Censored Bayesian Analysis of Visual Field Progression.Translational vision science & technology · 2021
    Article
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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

2 authors.

Andrew TurpinSchool of Computing & Information Systems, The University of Melbourne, Melbourne, Australia.
Allison M McKendrickDepartment of Optometry & Vision Sciences, The University of Melbourne, Melbourne, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Maps are required to relate visual field locations to optic nerve head regions. We compare individualized structure-to-function mapping (CUSTOM-MAP) to a population-derived mapping schema (POP-MAP). Methods: Maps were compared for 118 eyes with glaucomatous field loss, circumpapillary retinal nerve fiber layer (cpRNFL) thickness measured using spectral domain optical coherence tomography (OCT), and two landmarks: the optic nerve head (ONH) position relative to the fovea and the temporal raphe angle. Locations with visual field damage (total deviation < -6 dB) were mapped to 30° ONH sectors centered on the angle given by each mapping schema. The concordance between damaged function and damaged structure was determined per location for various cpRNFL damage probability levels, with the number of concordant locations divided by the total number of damaged field locations providing a concordance ratio per eye. Results: For the strictest concordance criteria (minimum cpRNFL thickness < 1% of normal), CUSTOM-MAP had higher mean concordance ratio than POP-MAP (60.5% c.f. 57.0% paired Wilcoxon, Conclusions: Anatomic parameters required for individualized structure-function mapping are readily measured with OCT and can provide improved concordance for some eyes. Translational Relevance: Personalizing structure-function mapping may improve concordance between these measures. We provide a web-based tool for creating customized maps.

Indexed as

Optic DiskNerve FibersRetinal Ganglion CellsVisual FieldsVisual Field Testsglaucomamappingoptical coherence tomographyperimetrystructure-functionvisual field

Identifiers

PMID33510958
PMCPMC7804493

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.