Evidence map›Paper›PMID 39464954›Full record

ArticleJournal of family medicine and primary care2024

A cross-sectional survey of ocular problems after COVID-19 infection among healthcare workers at a teaching hospital.

Santosh Kumar Mishra, Naveen Kumar, Arti Kumari, Anant Kumar Verma

Abstract read
In one paragraph

Article in Journal of family medicine and primary care, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

4 authors.

Santosh Kumar MishraDepartment of Ophthalmology, Patna Medical College, Patna, Bihar, India.
Naveen KumarDepartment of Ophthalmology, Patna Medical College, Patna, Bihar, India.
Arti KumariDepartment of Obstetrics and Gynecology, Nalanda Medical College, Patna, Bihar, India.
Anant Kumar VermaDepartment of Psychiatry, Bhagwan Mahavir Institute of Medical Sciences, Pawapuri, Nalanda, Bihar, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: The COVID-19 pandemic was caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV2). The impact of the pandemic is becoming more apparent now on various tissues and body organs. Limited knowledge is present about the characteristics, duration, and long-term sequelae of ocular problems post-COVID-19 infection. The purpose of the present study was to evaluate and analyze the ocular problems occurring in healthcare workers after COVID-19 infection. Material and Method: This was a cross-sectional survey study conducted in the Department of Ophthalmology, Patna Medical College and Hospital, Patna. The participants were healthcare workers who were exposed to COVID-19 infection ≥6 months back. A questionnaire was prepared. Responses were evaluated and analyzed. Patient consent and institutional committee permission were taken as per norms. Result: A total of 210 healthcare workers who were infected with COVID-19 >6 months back responded with complete answers. The maximum number of respondents was between the ages of 31 and 60 (73.3%). The pre-covid ocular problems were present in 25% of the participants, especially in those having chronic diseases (such as diabetes, hypertension, or both). Post-ocular problems were faced by 60% of the participants, which was significant ( Conclusion: A knowledge, understanding, and awareness about ocular problems and their long-term sequelae in post-COVID-19-infected patients will help in early diagnosis and better management of such patients.

Indexed as

COVID-19ocular problemsocular surface

Identifiers

PMID39464954
PMCPMC11504770

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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.