Evidence map›Paper›PMID 38672800›Full record

ArticleLife (Basel, Switzerland)2024

The Association between Ovarian Cancer and the Incidence of Newly Developed Dry Eye Disease: A Nationwide Population-Based Study.

Chia-Yi Lee, Shun-Fa Yang, Yu-Ling Chang, Jing-Yang Huang, Chao-Kai Chang

Open access · goldAbstract read
In one paragraph

Article in Life (Basel, Switzerland), 2024. 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
1.1field-weighted citation impact, top 23% of its field
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, 2 citations in OpenAlex.

  1. 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

5 authors at 4 institutions in 1 country.

Chia-Yi LeeInstitute of Medicine, Chung Shan Medical University, Taichung 402, Taiwan.
Shun-Fa YangInstitute of Medicine, Chung Shan Medical University, Taichung 402, Taiwan.ORCID 0000-0002-0365-7927
Yu-Ling ChangDepartment of Medical Education, Cathay General Hospital, Taipei 106, Taiwan.
Jing-Yang HuangDepartment of Medical Research, Chung Shan Medical University Hospital, Taichung 402, Taiwan.
Chao-Kai ChangNobel Eye Institute, Taipei 115, Taiwan.
Chung Shan Medical University Hospital · TWCathay General Hospital · TWDayeh University · TWJen-Ai Hospital · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We aim to investigate the potential correlation between the presence of ovarian cancer and the development of dry eye disease (DED) via the usage of the Longitudinal Health Insurance Database (LHID) of Taiwan. A retrospective cohort study was executed, and patients with ovarian cancer were selected according to the diagnostic and procedure codes. One ovarian cancer patient was matched to four non-ovarian cancer participants which served as control group, and a total of 4992 and 19,968 patients constructed the ovarian cancer and control groups, respectively. The primary outcome in the current study is the development of DED according to the diagnostic and procedure codes. Cox proportional hazard regression was utilized to produce the adjusted hazard ratio (aHR) and related 95% confidence interval (CI) of DED between the two groups. There were 542 and 2502 DED events observed in the ovarian cancer group and the control group, respectively. The ovarian cancer group illustrated a significantly higher incidence of DED development than the control group after the adjustment of several confounders (aHR: 1.10, 95% CI: 1.01-1.21,

Indexed as

agedry eye diseaseepidemiologyovarian canceroxidative stress

Identifiers

PMID38672800
PMCPMC11050760
OpenAlexW4395012215

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.