Evidence map›Paper›PMID 39829630›Full record

ArticleInternational journal of ophthalmology2025

Risk factors for biometry prediction error by Barrett Universal II intraocular lens formula in Chinese patients.

Xu-Hao Chen, Ying Hong, Xiang-Han Ke, Si-Jia Song, Yu-Jie Cen, Chun Zhang

Abstract read
In one paragraph

Article in International journal of ophthalmology, 2025. 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

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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.

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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

6 authors.

Xu-Hao ChenBeijing Key Laboratory of Restoration of Damaged Ocular Nerve, Department of Ophthalmology, Peking University Third Hospital, Beijing 100191, China.
Ying HongBeijing Key Laboratory of Restoration of Damaged Ocular Nerve, Department of Ophthalmology, Peking University Third Hospital, Beijing 100191, China.
Xiang-Han KeBeijing Key Laboratory of Restoration of Damaged Ocular Nerve, Department of Ophthalmology, Peking University Third Hospital, Beijing 100191, China.
Si-Jia SongBeijing Key Laboratory of Restoration of Damaged Ocular Nerve, Department of Ophthalmology, Peking University Third Hospital, Beijing 100191, China.
Yu-Jie CenBeijing Key Laboratory of Restoration of Damaged Ocular Nerve, Department of Ophthalmology, Peking University Third Hospital, Beijing 100191, China.
Chun ZhangBeijing Key Laboratory of Restoration of Damaged Ocular Nerve, Department of Ophthalmology, Peking University Third Hospital, Beijing 100191, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimTo investigate the influence of postoperative intraocular lens (IOL) positions on the accuracy of cataract surgery and examine the predictive factors of postoperative biometry prediction errors using the Barrett Universal II (BUII) IOL formula for calculation.

methodsThe prospective study included patients who had undergone cataract surgery performed by a single surgeon from June 2020 to April 2022. The collected data included the best-corrected visual acuity (BCVA), corneal curvature, preoperative and postoperative central anterior chamber depths (ACD), axial length (AXL), IOL power, and refractive error. BUII formula was used to calculate the IOL power. The mean absolute error (MAE) was calculated, and all the participants were divided into two groups accordingly. Independent

resultsA total of 261 patients were enrolled. The 243 (93.1%) and 18 (6.9%) had postoperative MAE<1 and >1 D, respectively. The number of females was higher in patients with MAE>1 D (

conclusionRisk factors for biometry prediction error after cataract surgery include the patient's sex and postoperative ACD. Patients with a shallow postoperative anterior chamber are prone to have refractive errors.

Indexed as

anterior chamber depthbiometry prediction errorgenderintraocular lens power calculation

Identifiers

PMID39829630
PMCPMC11672096

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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.