Evidence map›Paper›PMID 34488617›Full record

ArticleThe journal of physiological sciences : JPS2021

Pain expectation and avoidance in the social context: an electrophysiological study.

Alessandro Piedimonte, Denisa Adina Zamfira, Giulia Guerra, Sergio Vighetti, Elisa Carlino

Open access · goldAbstract read
In one paragraph

Article in The journal of physiological sciences : JPS, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.2field-weighted citation impact, top 46% 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

4 citing papers in PubMed, 7 citations in OpenAlex.

  1. Trial
  2. Article
  3. Article
  4. 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

5 authors at 1 institution in 1 country.

Alessandro PiedimonteDepartment of Neuroscience "Rita Levi Montalcini", University of Turin, 10125, Turin, TO, Italy.
Denisa Adina ZamfiraDepartment of Neuroscience "Rita Levi Montalcini", University of Turin, 10125, Turin, TO, Italy.
Giulia GuerraDepartment of Neuroscience "Rita Levi Montalcini", University of Turin, 10125, Turin, TO, Italy.
Sergio VighettiDepartment of Neuroscience "Rita Levi Montalcini", University of Turin, 10125, Turin, TO, Italy.
Elisa CarlinoDepartment of Neuroscience "Rita Levi Montalcini", University of Turin, 10125, Turin, TO, Italy. elisa.carlino@unito.it.
University of Turin · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Contingent negative variation (CNV) is an informative electrophysiological measure of pain anticipation showing higher amplitudes when highly painful stimulation is expected while presenting lower amplitudes when low painful stimulation is expected. Two groups of participants were recruited: one group expected and received an electrical stimulation of different intensities while being alone in the room (i.e. without social context), while a second group performed the same experiment with an observer in the room (i.e. with social context). Lower pain ratings and slower reaction times were observed in the group with social context and these results were accompanied in this group by a lower amplitude in the early component of the CNV as well as a lower amplitude of the later component of the wave. These results show that CNV can be considered a precise measure of central elaboration of pain anticipation explaining both its perceptual and motor components.

Indexed as

Contingent Negative VariationMotivationElectric StimulationHumansPainReaction TimeContingent negative variationElectrophysiologyMotor responsesPainSocial learning

Identifiers

PMID34488617
PMCPMC10717249
OpenAlexW3198642299

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.