Evidence map›Paper›PMID 41250303›Full record

ArticleJournal of cataract and refractive surgery2026

Retrospective analysis of U.S. Food and Drug Administration recalls of artificial tear solutions from 2014 to 2025.

Aishwarya Bhattacharya, Paul C Kang

Abstract read
In one paragraph

Article in Journal of cataract and refractive surgery, 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

2 authors.

Aishwarya BhattacharyaFrom the Yale School of Medicine, New Haven, Connecticut (Bhattacharya); Department of Ophthalmology and Visual Science, Yale School of Medicine, New Haven, Connecticut (Kang).ORCID 0000-0001-5051-3355
Paul C Kang

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo examine all recalls of artificial tears from 2014 to 2025 to identify the most common types, classes of products, and associated reasons for recalls, and to provide guidance on safety.

settingUnited States.

designRetrospective epidemiologic assessment.

methodsArtificial tear recalls from January 2014 to August 2025 were identified through the U.S. Food and Drug Administration Enforcement Report. Data on country of manufacture, classification, ingredients, preservative use, and recall reason were analyzed. The primary end points of the study were the most common causes of recall, and the types of products associated with each recall. Secondary end points included classification of recall reasons, common ingredients associated with recalls, and the country where the product was manufactured.

results110 unique formulations of recalled tears were identified. Most recalls were class II (81) linked to lack of assurance of sterility (65) that could cause reversible adverse health outcomes and class I (26) due to microbial contamination (25) that could cause permanent adverse outcomes. Preserved (48) and preservative-free tears (62) were similarly implicated (χ 2 (3, N = 110) = 6.71, P = .082). Most recalled tears were manufactured in the United States (85) and were generic formulations (80).

conclusionsMost recalled tears were U.S. generics recalled because of lack of sterility. Preservative-free and preserved tears had similar recalls. Overall, recalls tied to confirmed microbial contamination of tears remain rare compared with their prolific use in the U.S.

Indexed as

Drug RecallsLubricant Eye DropsOphthalmic SolutionsUnited States Food and Drug AdministrationHumansPreservatives, PharmaceuticalRetrospective StudiesUnited StatesLubricant Eye DropsOphthalmic SolutionsPreservatives, Pharmaceutical

Identifiers

PMID41250303
PMCPMC12998752

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Registered trials

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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.