Evidence map›Paper›PMID 31968563›Full record

ArticleJournal of clinical medicine2020

Pain Catastrophising Predicts Alcohol Hangover Severity and Symptoms.

Sam Royle, Lauren Owen, David Roberts, Lynne Marrow

Open access · goldAbstract read
In one paragraph

Article in Journal of clinical medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.6field-weighted citation impact, top 16% 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

9 citing papers in PubMed, 15 citations in OpenAlex.

  1. Trial
  2. Article
  3. Hangover headache and its behavioral changes in rats.Iranian journal of basic medical sciences · 2023
    Article
  4. Article
  5. Alcohol Hangover Across the Lifespan: Impact Of Sex and Age.Alcohol and alcoholism (Oxford, Oxfordshire) · 2021
    Article
  6. The Relationship between Pain Sensitivity, Pain Catastrophizing and Hangover Severity.International journal of environmental research and public health · 2021
    Article
  7. Article
  8. Article
  9. 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

4 authors at 1 institution in 1 country.

Sam RoyleDepartment of Psychology, School of Health and Society, University of Salford, Frederick Road, Salford M6 6PU, UK.
Lauren OwenDepartment of Psychology, School of Health and Society, University of Salford, Frederick Road, Salford M6 6PU, UK.
David RobertsDepartment of Psychology, School of Health and Society, University of Salford, Frederick Road, Salford M6 6PU, UK.
Lynne MarrowDepartment of Psychology, School of Health and Society, University of Salford, Frederick Road, Salford M6 6PU, UK.
University of Salford · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Alcohol hangover is a cause of considerable social and economic burden. Identification of predictors of alcohol hangover severity have the potential to contribute to reductions in costs associated with both absenteeism/presenteeism and health care. Pain catastrophising (PC) is the tendency to ruminate and describe a pain experience in more exaggerated terms. The current study examines the possibility that this cognitive coping strategy may influence experience of alcohol hangover. The aims of the current study were to (1) examine the relationship between hangover severity and PC, (2) explore and identify discreet factors within the Acute Hangover Scale (AHS) and (3) explore whether independent factors/dimensions of acute hangover are differentially predicted by PC. A retrospective survey (

Indexed as

acute hangover scalealcoholcatastrophisinghangoverveisalgia

Identifiers

PMID31968563
PMCPMC7019682
OpenAlexW2999021068

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.