Evidence map›Paper›PMID 27583896›Full record

Observational studyMedicine2016

Algorithm of chest wall keloid treatment.

Xiao Long, Mingzi Zhang, Yang Wang, Ru Zhao, Youbin Wang, Xiaojun Wang

Open access · goldAbstract readCase ReportsObservational Study
In one paragraph

Observational study in Medicine, 2016. 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 21% 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, 17 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.

Xiao LongDepartment of Plastic Surgery, Peking Union Medical College Hospital, Beijing, China.
Mingzi Zhang
Yang Wang
Ru Zhao
Youbin Wang
Xiaojun Wang
Chinese Academy of Medical Sciences & Peking Union Medical College · CNPeking Union Medical College Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Keloids are common in the Asian population. Multiple or huge keloids can appear on the chest wall because of its tendency to develop acne, sebaceous cyst, etc. It is difficult to find an ideal treatment for keloids in this area due to the limit of local soft tissues and higher recurrence rate. This study aims at establishing an individualized protocol that could be easily applied according to the size and number of chest wall keloids.A total of 445 patients received various methods (4 protocols) of treatment in our department from September 2006 to September 2012 according to the size and number of their chest wall keloids. All of the patients received adjuvant radiotherapy in our hospital. Patient and Observer Scar Assessment Scale (POSAS) was used to assess the treatment effect by both doctors and patients. With mean follow-up time of 13 months (range: 6-18 months), 362 patients participated in the assessment of POSAS with doctors.Both the doctors and the patients themselves used POSAS to evaluate the treatment effect. The recurrence rate was 0.83%. There was an obvious significant difference (P < 0.001) between the before-surgery score and the after-surgery score from both doctors and patients, indicating that both doctors and patients were satisfied with the treatment effect.Our preliminary clinical result indicates that good clinical results could be achieved by choosing the proper method in this algorithm for Chinese patients with chest wall keloids. This algorithm could play a guiding role for surgeons when dealing with chest wall keloid treatment.

Indexed as

AlgorithmsAdultAsian PeopleFemaleHumansKeloidMaleMiddle AgedPatient Reported Outcome MeasuresRadiotherapy, AdjuvantRecurrenceSkin TransplantationSurgical FlapsSuture TechniquesThoracic WallTreatment Outcome

Identifiers

PMID27583896
PMCPMC5008580
OpenAlexW2518061257

What OpenQuestion holds

Textmetadata
LicenceCC BY-SA
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.