Evidence map›Paper›PMID 41601748›Full record

ReviewFrontiers in medicine2025

Ocular surface disease following LASIK and cataract surgery: a review of their interrelated complications.

Matthew D Spangler, Nila Kirupaharan, John D Sheppard

Abstract readReview
In one paragraph

Review in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
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

3 authors.

Matthew D SpanglerDrexel University College of Medicine, Philadelphia, PA, United States.
Nila KirupaharanDrexel University College of Medicine, Philadelphia, PA, United States.
John D SheppardVirginia Eye Consultants, Norfolk, VA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Ocular surface disease is a multifactorial condition that is very commonly caused by dry eye disease (DED). Ophthalmic procedures intended to improve visual outcomes, laser-assisted Areas covered: This review examines the pathophysiological mechanisms contributing to ocular surface disease development following LASIK and cataract surgery. Both procedures are associated with the transection of corneal nerves, leading to decreased tear production, surface instability, altered neurotrophin production, and impairment of the blink reflex. Furthermore, these incisional procedures stress ocular tissues, while the use of preservatives in topical medications administered during or after these procedures may exacerbate dry eye symptoms. Management strategies for postoperative dry eye disease include, but are not limited to, the use of artificial tears, anti-inflammatory agents such as cyclosporine A or lifitegrast, nerve growth factor therapies, and procedural interventions including punctal plugs, thermal pulsation, lid exfoliation, and intense pulsed light therapy. Additionally, artificial intelligence has emerged as a promising tool to enhance the selection of optimal candidates, thereby minimizing risk and optimizing postoperative outcomes and improving patient satisfaction. Discussion: LASIK and cataract surgery are highly effective vision correction procedures that have constantly evolved to improve visual outcomes and mitigate side effects. However, ocular surface diseases remain a common complication affecting the outcome and quality of life of post-operative patients. The integration of personalized treatment and AI-based screening protocols can help improve patient outcomes.

Indexed as

artificial intelligencecataractdry eye diseaseLASIKsurgery

Identifiers

PMID41601748
PMCPMC12833407

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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.