Evidence map›Paper›PMID 38223614›Full record

Trial reportAfrican health sciences2023

Effect of whole course seamless nursing mode on patients with chronic infectious wounds.

Yun Peng, Chenlu Gao, Yan Sun

Open access · diamondAbstract readRandomized Controlled Trial
In one paragraph

Trial report in African health sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.8field-weighted citation impact, top 25% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. 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

3 authors at 2 institutions in 1 country.

Yun PengQingdao Central Hospital, 127 Siliu South Road, Qingdao 266000, Shandong Province, China.
Chenlu GaoQingdao Central Hospital, 127 Siliu South Road, Qingdao 266000, Shandong Province, China.
Yan SunQingdao Central Hospital, 127 Siliu South Road, Qingdao 266000, Shandong Province, China.
Qingdao Municipal Hospital · CNQingdao University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Chronic infectious wounds seriously affect patients' quality of life. Aim: To assess the effect of whole course seamless nursing mode on patients with chronic infectious wounds. Methodology: One hundred patients treated between January 2019 and December 2020 were randomly divided into control and observation groups (n=50) that were given routine nursing and whole course seamless nursing, respectively. Their pain score, comfort score, wound healing time, wound healing effect, psychological state scores, sleep indices, quality-of-life scores and degree of satisfaction with nursing were compared. Results: Observation group had lower pain score and higher comfort score than those of control group after nursing (P<0.05). Compared with control group, observation group had shorter wound healing time and higher grade-A wound healing rate (P<0.05). The SDS and SAS scores of observation group were lower than those of control group (P<0.05). Observation group also had significantly shorter sleep latency, longer actual sleep time, lower PSQI score, as well as higher quality-of-life score and overall satisfaction rate than those of control group (P<0.05). Conclusion: For patients with chronic infectious wounds, whole course seamless nursing effectively relieves wound pain, facilitates wound healing, improves comfort, psychological state and sleep status, and makes them more satisfied.

Indexed as

PainQuality of LifeHumansSleepChronic infectious woundquality of lifewhole course seamless nursing

Identifiers

PMID38223614
PMCPMC10782296
OpenAlexW4387584822

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.