Evidence map›Paper›PMID 36213584›Full record

Trial reportComputational and mathematical methods in medicine2022

A Prognostic Model for the Respiratory Function of Patients with Nonsevere Pulmonary Infection Based on Breathing Exercises and Acupuncture Therapy: Development and Validation.

Yulin Shi, Yong Hu, Guomeng Xu, Yaoqi Ke

Open access · hybridAbstract readRandomized Controlled Trial
In one paragraph

Trial report 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. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.3field-weighted citation impact, top 34% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

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

4 authors at 1 institution in 1 country.

Yulin ShiDepartment of Rehabilitation Medicine, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, China.ORCID https://orcid.org/0000-0003-2227-6457
Yong HuDepartment of Rehabilitation Medicine, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, China.ORCID https://orcid.org/0000-0003-4258-377X
Guomeng XuDepartment of Rehabilitation Medicine, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, China.ORCID https://orcid.org/0000-0003-1277-8365
Yaoqi KeDepartment of Respiratory Medicine, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, China.ORCID https://orcid.org/0000-0002-0370-421X
Hubei University of Arts and Science · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: In this study, a prognostic model for the respiratory function was constructed based on the treatment methods of patients with nonsevere pulmonary infection, aiming to provide a reference for clinical decision-making. Method: A total of 500 patients with nonsevere pulmonary infection were included in this study. The patients were randomized into training set ( Results: Results of the ordinal multinomial logistic regression model showed that age ( Conclusion: The prognostic model for respiratory function of patients with nonsevere pulmonary infection constructed in this study had favorable predictive performance, which is of great significance in the clinical nursing and treatment of patients with pulmonary infection.

Indexed as

Acupuncture TherapyPneumoniaBreathing ExercisesForced Expiratory VolumeHumansPrognosisReproducibility of ResultsRespiratory Function TestsVital Capacity

Identifiers

PMID36213584
PMCPMC9536990
OpenAlexW4297968756

What OpenQuestion holds

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LicenceCC BY
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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.