Evidence map›Paper›PMID 25785160›Full record

ArticleInternational journal of clinical and experimental medicine2015

Arsenical keratosis caused by medication: a case report and literature.

Sijing Zhou, Junsheng Zhou, Shengping Liu, Ran Wang, Zaixing Wang

Open access · greenAbstract readCase Reports
In one paragraph

Article in International journal of clinical and experimental medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
0.8field-weighted citation impact, top 29% 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

2 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.

  1. Pooled it
  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

5 authors at 2 institutions in 1 country.

Sijing ZhouHefei Prevention and Treatment Center for Occupational Diseases Hefei 230022, P. R. China ; Department of Respiratory Medicine, The First Affiliated Hospital of Anhui Medical University Hefei 230022, P. R. China.
Junsheng ZhouHefei Prevention and Treatment Center for Occupational Diseases Hefei 230022, P. R. China.
Shengping LiuHefei Prevention and Treatment Center for Occupational Diseases Hefei 230022, P. R. China.
Ran WangDepartment of Respiratory Medicine, The First Affiliated Hospital of Anhui Medical University Hefei 230022, P. R. China.
Zaixing WangDepartment of Dermatology, The First Affiliated Hospital of Anhui Medical University Hefei 230022, P. R. China.
Anhui Medical University · CNHefei University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Medication-induced arsenical keratosis is a rare type of arsenical keratosis. We describe here a case of 70-year-old man to explore the clinical characters, diagnosis and treatment of medication-induced arsenical keratosis in order to improve the understanding of this disease and reduce the misdiagnosis rate. The clinical characters, signs, lab findings as well as progression, diagnosis and treatment in the case of arsenical keratosis were analyzed. The patient of medication-induced arsenical keratosis suffered from chronic eczema. He has taken realgar during the treatment. His medication caused arsenical keratosis. Medication-induced arsenical keratosis is rare. Making the medication history clear and using urine arsenic detection if necessary are of significance to understand the patients' condition. It is quite effective that using Sodium Dimercaptosulphonate during the treatment without delay.

Indexed as

arsenical keratosisarsenic trioxideHerbal medication-induced

Identifiers

PMID25785160
PMCPMC4358615
OpenAlexW1526304218

What OpenQuestion holds

Textmetadata
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.