Evidence map›Paper›PMID 34880967›Full record

ArticleDermatology reports2021

Post-acne hyperpigmentation: Evaluation of risk factors and the use of artificial neural network as a predictive classifier.

Firas Al-Qarqaz, Khaldon Bodoor, Ala Baba, Ali Al-Yousef, Jihan Muhaidat, Diala Alshiyab

Open access · goldAbstract read
In one paragraph

Article in Dermatology reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.9field-weighted citation impact, top 14% 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

10 citing papers in PubMed, 15 citations in OpenAlex.

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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

6 authors at 2 institutions in 1 country.

Firas Al-QarqazDepartment of Dermatology, Jordan University of Science and Technology, Irbid.
Khaldon BodoorDepartment of Biotechnology and Genetic Engineering, Faculty of Science, Jordan University of Science and Technology, Irbid.
Ala BabaDepartment of Dermatology, Jordan University of Science and Technology, Irbid.
Ali Al-YousefComputer Department, Computer and Information Technology College, Jerash University, Jerash, Jordan.
Jihan MuhaidatDepartment of Dermatology, Jordan University of Science and Technology, Irbid.
Diala AlshiyabDepartment of Dermatology, Jordan University of Science and Technology, Irbid.
Jordan University of Science and Technology · JOJerash University · JO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Acne is common among young individuals. People with dark skin have a higher risk for developing pigmentary complications. Inflammation is an important factor in post-acne hyperpigmentation however other factors are also involved in developing this complication however these factors are not well studied. The aim of this study is to identify risk factors involved in post-acne hyperpigmentation. Clinical data related to acne, acne- related hyperpigmentation were collected. Data was analyzed for risk factors associated with acne pigmentation. Artificial neural network was used as predictive disease classifier for the outcome of pigmentation. Majority of patients in this study (339 patients) had dark skin phototypes (3 and 4). Post- acne hyperpigmentation was seen in more than 80% of patients. Females, darker skin color, severe acne, facial sites, and excessive sunlight exposure, squeezing or scratching lesions are important risk factors for post-acne hyperpigmentation. Post-acne hyperpigmentation is multifactorial. Several factors implicated in PAH are modifiable by adequate patient education (lesion trauma, excessive sunlight exposure). The use of ANN was helpful in predicting appearance of post-acne hyperpigmentation based on identified risk factors.

Indexed as

AcneArtificial neural networkClassificationPost-acne hyperpigmentation

Identifiers

PMID34880967
PMCPMC8611517
OpenAlexW3204625224

What OpenQuestion holds

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LicenceCC BY-NC
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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.