Evidence map›Paper›PMID 29874239›Full record

Trial reportPloS one2018

Development and pilot-testing of the Alopecia Areata Assessment Tool (ALTO).

David G Li, Kathie P Huang, Fan Di Xia, Cara Joyce, Deborah A Scott, Abrar A Qureshi, Arash Mostaghimi

Open access · goldAbstract readClinical Trial
In one paragraph

Trial report in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.0field-weighted citation impact, top 24% 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

5 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
  5. 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

7 authors at 3 institutions in 1 country.

David G LiDepartment of Dermatology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
Kathie P HuangDepartment of Dermatology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
Fan Di XiaDepartment of Dermatology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
Cara JoyceLoyola University, Chicago, Illinois, United States of America.
Deborah A ScottDepartment of Dermatology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
Abrar A QureshiDepartment of Dermatology, Alpert Medical School of Brown University, Providence, Rhode Island, United States of America.
Arash MostaghimiDepartment of Dermatology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.ORCID 0000-0002-6084-5617
Brigham and Women's Hospital · USBrown University · USLoyola University Chicago · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlopecia areata (AA) is an autoimmune disease characterized by non-scarring hair loss. The lack of a definitive biomarker or formal diagnostic criteria for AA limits our ability to define the epidemiology of the disease. In this study, we developed and tested the Alopecia Areata Assessment Tool (ALTO) in an academic medical center to validate the ability of this questionnaire in identifying AA cases.

methodsThe ALTO is a novel, self-administered questionnaire consisting of 8 closed-ended questions derived by the Delphi method. This prospective pilot study was administered during a 1-year period in outpatient dermatology clinics. Eligible patients (18 years or older with chief concern of hair loss) were recruited consecutively. No patients declined to participate. The patient's hair loss diagnosis was determined by a board-certified dermatologist. Nine scoring algorithms were created and used to evaluate the accuracy of the ALTO in identifying AA.

results239 patients (59 AA cases and 180 non-AA cases) completed the ALTO and were included for analysis. Algorithm 5 demonstrated the highest sensitivity (89.8%) while algorithm 3 demonstrated the highest specificity (97.8%). Select questions were also effective in clarifying disease phenotype.

conclusionIn this study. we have successfully demonstrated that ALTO is a simple tool capable of discriminating AA from other types of hair loss. The ALTO may be useful to identify individuals with AA within large populations.

Indexed as

Surveys and QuestionnairesAdolescentAdultAgedAlopecia AreataFemaleHumansMaleMiddle AgedPilot Projects

Identifiers

PMID29874239
PMCPMC5991373
OpenAlexW2805733344

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.