Evidence map›Paper›PMID 39508499›Full record

ArticleActa dermato-venereologica2024

The Importance of Readability: A Guide to Understanding Alopecia Areata through Multilingual Online Resources.

Tomasz Skrzypczak, Anna Skrzypczak, Jacek C Szepietowski

Abstract read
In one paragraph

Article in Acta dermato-venereologica, 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
–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

3 authors.

Tomasz SkrzypczakUniversity Hospital in Wroclaw, Wroclaw, Poland.
Anna SkrzypczakFaculty of Dentistry, Wroclaw Medical University, Wroclaw, Poland.
Jacek C SzepietowskiFaculty of Medicine, Wroclaw University of Science and Technology, Wroclaw, Poland. jacek.szepietowski.work@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Online resources play a vital role in patient education, yet the readability of alopecia areata-related materials remained understudied. A thorough analysis of online alopecia areata-related materials across 5 languages was conducted using Google search. Search terms "alopecia areata" and "alopecia areata treatment" were translated and queried, generating search result lists. The first 50 articles from each list were evaluated for suitability. The materials were categorized into 2 main groups: those focusing on alopecia areata itself and those addressing its treatment. Treatment materials were further divided into subgroups, including Janus kinase inhibitors and other treatment options. Readability was evaluated using the Lix score. The analysis included 251 articles in English, German, French, Italian, and Spanish. The overall mean Lix score was 52 ± 8, which classified them as very hard to comprehend. Articles on alopecia areata treatment had a mean Lix score of 55 ± 8, which was significantly higher (p < 0.001) than those on alopecia areata itself, 50 ± 8. alopecia areata-treatment articles dedicated to JAK inhibitors had an average Lix score of 57 ± 10 and it was significantly higher (p = 0.043) than those on other treatment, 53 ± 6. Online resources on alopecia areata and its treatments remained challenging to comprehend, particularly regarding JAK inhibitors. Improving clarity in patient education materials is crucial for informed decision-making and therapeutic relationships.

Indexed as

Alopecia AreataComprehensionHealth LiteracyInternetPatient Education as TopicConsumer Health InformationHumansJanus Kinase InhibitorsJanus Kinase Inhibitors

Identifiers

PMID39508499
PMCPMC11559260

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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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.