Evidence map›Paper›PMID 38275811›Full record

ReviewAnimals : an open access journal from MDPI2024

Pain Assessment in Cattle by Use of Numerical Rating and Visual Analogue Scales-A Systematic Review and Meta-Analysis.

Theresa Tschoner, Kristina R Mueller, Yury Zablotski, Melanie Feist

Abstract readReview
In one paragraph

Review in Animals : an open access journal from MDPI, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
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

6 citing papers in PubMed.

  1. CowPainCheck: AI-based facial expression analysis for dairy cow welfare.Journal of animal science and technology · 2026
    Review
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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.

Theresa TschonerClinic for Ruminants with Ambulatory and Herd Health Services at the Centre for Clinical Veterinary Medicine, Ludwig-Maximilians-Universität Munich, Sonnenstrasse 16, 85764 Oberschleißheim, Germany.ORCID 0000-0001-9640-9190
Kristina R MuellerSchool of Veterinary Science, Massey University, Private Bag 11 222, Palmerston North 4474, New Zealand.
Yury ZablotskiClinic for Ruminants with Ambulatory and Herd Health Services at the Centre for Clinical Veterinary Medicine, Ludwig-Maximilians-Universität Munich, Sonnenstrasse 16, 85764 Oberschleißheim, Germany.ORCID 0000-0001-6928-4089
Melanie FeistClinic for Ruminants with Ambulatory and Herd Health Services at the Centre for Clinical Veterinary Medicine, Ludwig-Maximilians-Universität Munich, Sonnenstrasse 16, 85764 Oberschleißheim, Germany.

Funding

Deutsche Forschungsgemeinschaft 505835300
6 · The paper itself

Abstract

Subjective pain assessment in cattle is contingent upon the observer's experience and attitude. Studies of pain assessment in cattle by veterinarians and farmers using different pain scales have been published. This systematic review and meta-analysis aimed to describe and compare the pain scores given by veterinarians and producers for different procedures and conditions using either a NRS or VAS. The literature search was conducted with PubMed (MEDLINE) and Agricola, using defined search terms (e.g., peer-reviewed). A total of 842 articles were identified. After screening of duplicates, abstracts, and full texts, a total of 16 articles were included in this systematic review. Different pain scales were used for the included studies (NRS 0-10 for eight studies, NRS 1-10 for six studies, NRS 1-10 and VAS 0-10 for one study, and VAS 0-1 for one study). Most studies (

Indexed as

acute painanalgesiacalvesdairy cattlefarmersNRSpain managementpain scoringsurveyVASveterinarians

Identifiers

PMID38275811
PMCPMC10812761

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.