Evidence map›Paper›PMID 41328723›Full record

ArticleActa ophthalmologica2026

Corneal power modelling with OCT data - Thin and thick lens paraxial models versus raytracing.

Achim Langenbucher, Nóra Szentmáry, Alan Cayless, Peter Hoffmann, Jascha Wendelstein

Abstract read
In one paragraph

Article in Acta ophthalmologica, 2026. 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

5 authors.

Achim LangenbucherDepartment of Experimental Ophthalmology, Saarland University, Homburg (Saar), Germany.ORCID https://orcid.org/0000-0001-9175-6177
Nóra SzentmáryDepartment of Experimental Ophthalmology, Saarland University, Homburg (Saar), Germany.ORCID https://orcid.org/0000-0001-8019-1481
Alan CaylessSchool of Physical Sciences, The Open University, Milton Keynes, UK.
Peter HoffmannAugen- und Laserklinik Castrop-Rauxel, Castrop-Rauxel, Germany.
Jascha WendelsteinDepartment of Experimental Ophthalmology, Saarland University, Homburg (Saar), Germany.ORCID https://orcid.org/0000-0003-4145-2559

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEvaluating keratometric power with Zeiss index (PKZ), paraxial thick cornea power (Gullstrand [PG]) and power referenced to the front (PFV) and back vertex plane (PBV) and raytracing power (PR), and modelling the deviation from PKZ with a multivariable linear prediction model.

methodsA dataset of 4604 Casia2 measurements from a cataractous population was Copula expanded to N = 30 000 maintaining the individual univariate distributions and interactions. PKZ was compared with paraxial thick lens corneal power PG, PFV, PBV, and PR using measured pupil size and variations of apertures from 1 to 6 mm.

resultsOn average, PKZ/PG/PFV/PBV was 43.05/42.74/42.83/43.59 D, and PR was 43.03 D with the measured pupil size. Varying pupil size from 1 (1) 6 mm increased mean PR with aperture size (42.84/42.91/43.02/43.17/43.37/43.62 D). The multivariate linear models predicting the deviation of PG-PKZ, PFV-PKZ, and PBV-PKZ with corneal radii and central thickness performed well, with R

conclusionCorneal power derived using the paraxial thick cornea model differs from keratometric power and a linear model performs well in predicting the differences. However, raytracing power differs even more from keratometry with the linear model far less effective.

Indexed as

CataractCorneaLens, CrystallineRefraction, OcularTomography, Optical CoherenceHumanscorneal powerkeratometryMonte Carlo simulationmultivariable linear modelparaxial thick cornea modelraytracing

Identifiers

PMID41328723
PMCPMC13353762

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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.