Evidence map›Paper›PMID 35898487›Full record

SynthesisComputational and mathematical methods in medicine2022

A Net Meta-Analysis of the Effectiveness of Different Types of Dressings in the Treatment of Diabetic Foot.

Ying Wang, Nan Chao, Doudou Yin

RetractedOpen access · hybridAbstract readMeta-AnalysisRetracted Publication
In one paragraph

Synthesis in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 6 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 2 pooled it
1.0field-weighted citation impact, top 23% 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

6 citing papers in PubMed, 2 syntheses or guidelines pooled it, 7 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Review
  4. Journal of clinical medicine · 2023
    Article
  5. Efficacy of 15% lysine cream in treating diabetic foot ulcers: a randomized interventional study.International journal of physiology, pathophysiology and pharmacology · 2023
    Article
  6. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Ying WangInternal Medicine, Changzhou Medical District, No. 904 Hospital the Joint Logistics Support Force of the Chinese People's Liberation Army, 213000, China.ORCID https://orcid.org/0000-0002-7836-829X
Nan ChaoInternal Medicine, Changzhou Medical District, No. 904 Hospital the Joint Logistics Support Force of the Chinese People's Liberation Army, 213000, China.
Doudou YinInternal Medicine, Changzhou Medical District, No. 904 Hospital the Joint Logistics Support Force of the Chinese People's Liberation Army, 213000, China.
Chinese People's Liberation Army · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This is an analysis of the impact of a new dressing commonly used to treat diabetic foot (DFU). Methods: Chinese and English databases were searched to collect clinical randomized controlled studies (RCTs) of various types of dressings for the treatment of DFU, and the healing rates of the different dressings were combined by reticulation meta-analysis. Results: The aggregate of the 36 RCTs included in this study analysed the healing rates of nine dressings: conventional dressing, alginate dressing, chitosan dressing, hyaluronic acid dressing, platelet-rich plasma dressing, amniotic membrane dressing, honey dressing, human recombinant growth factor dressing, and silver ionomer dressing. Conclusion: Hyaluronic acid dressing, amniotic membrane dressing, honey dressing, and platelet-rich plasma dressing are the ideal materials for topical treatment of DFU.

Indexed as

Diabetes MellitusDiabetic FootBandages, HydrocolloidHumansHyaluronic AcidWound HealingHyaluronic Acid

Identifiers

PMID35898487
PMCPMC9313931
OpenAlexW4285802687

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.