Evidence map›Paper›PMID 33362897›Full record

ArticleMedical acupuncture2020

Electroacupuncture for Treating Chronic Low-Back Pain: Preliminary Research Results.

Jiang-Ti Kong

Open access · greenAbstract read
In one paragraph

Article in Medical acupuncture, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 2 pooled it
0.2field-weighted citation impact, top 36% 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

3 citing papers in PubMed, 2 syntheses or guidelines pooled it, 4 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. 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

1 author at 1 institution in 1 country.

Jiang-Ti KongDepartment of Anesthesiology, Perioperative and Pain Medicine, Division of Pain Medicine, Stanford University School of Medicine, Stanford, California, USA.
Stanford Medicine · US

Funding

Predicting Analgesic Response to Acupuncture - A Practical ApproachK23AT008477 · NCCIH · STANFORD UNIVERSITY · PI KONG, JIANG-TI · 2015 to 2019
$739k
NCCIH NIH HHS K23 AT008477
6 · The paper itself

Abstract

This report, prepared by a lead researcher, describes 2 independent, but similarly designed, clinical trials that were conducted to investigate the effectiveness, mechanisms, and predictors of electroacupuncture (EA) for treating chronic low-back pain (CLBP). Both trials recruited adults (ages 21-65) who had CLBP with an intensity ≥4/10 and a duration ≥6 months. Verum EA or sham EA was administered twice per week for 6-8 weeks. The common outcome between the 2 studies was the patients' responses to the Roland Morris Disability Questionnaire. Using least absolute shrinkage and selection operator (LASSO), the authors were able to predict clinical outcome in the second study by using a prediction model based on data from the first study. This work demonstrated the feasibility of predicting clinical outcomes when using acupuncture for treating CLBP.

Indexed as

chronic low-back painelectroacupuncturemachine-learningprediction

Identifiers

PMID33362897
PMCPMC7755842
OpenAlexW3111696936

What OpenQuestion holds

Textmetadata
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.