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Animals
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Advances in Pain Management in Large Animals
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Advances in Pain Management in Large Animals

A special issue of Animals (ISSN 2076-2615). This special issue belongs to the section "Veterinary Clinical Studies".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 4755

Special Issue Editor

Dr. Klaus Hopster

E-Mail Website
Guest Editor
School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA, USA
Interests: epidural analgesia

Special Issue Information

Dear Colleagues,

As the field of veterinary medicine grows, so do the opportunities to improve pain recognition and management in large animals. It is not only our first duty as veterinarians but also an ethical obligation to prevent harm and treat pain and discomfort in these animals.

Furthermore, these strategies have been shown to improve performance in large animal athletes and production animals.

The veterinary literature concerning pain recognition strategies and the efficacy and safety of analgesic measures in large animals is still lacking. Drug choices, doses, and treatment durations and intervals are often provided empirically, extrapolating data from research on humans.

This Special Issue on “Advances in Pain Management in Large Animals” aims to present recent research and reviews from this area in order to stimulate interest, understanding, and exploration of this important field.

Original manuscripts concerning pain management in large animals, including farm animals, are invited to participate in this Special Issue, particularly those that: (1) provide strategies and tools for the recognition and quantification of pain and (2) demonstrate the efficacy and safety of new pain management strategies.

Dr. Klaus Hopster
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Animals is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • pain
  • analgesia
  • pharmacology
  • efficacy
  • horse
  • cattle
  • swine
  • bovine
  • ruminant

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

23 pages, 3999 KiB  
Article
Reliability and Validity of UNESP-Botucatu Cattle Pain Scale and Cow Pain Scale in Bos taurus and Bos indicus Bulls to Assess Postoperative Pain of Surgical Orchiectomy
by Rubia M. Tomacheuski, Alice R. Oliveira, Pedro H. E. Trindade, Flávia A. Oliveira, César P. Candido, Francisco J. Teixeira Neto, Paulo V. Steagall and Stelio P. L. Luna
Animals 2023, 13(3), 364; https://doi.org/10.3390/ani13030364 - 20 Jan 2023
Cited by 3 | Viewed by 2295
Abstract
Pain assessment guides decision-making in pain management and improves animal welfare. We aimed to investigate the reliability and validity of the UNESP-Botucatu cattle pain scale (UCAPS) and the cow pain scale (CPS) for postoperative pain assessment in Bos taurus (Angus) and Bos indicus [...] Read more.
Pain assessment guides decision-making in pain management and improves animal welfare. We aimed to investigate the reliability and validity of the UNESP-Botucatu cattle pain scale (UCAPS) and the cow pain scale (CPS) for postoperative pain assessment in Bos taurus (Angus) and Bos indicus (Nelore) bulls after castration. Methods: Ten Nelore and nine Angus bulls were anaesthetised with xylazine–ketamine–diazepam–isoflurane–flunixin meglumine. Three-minute videos were recorded at -48 h, preoperative, after surgery, after rescue analgesia and at 24 h. Two evaluators assessed 95 randomised videos twice one month apart. Results: There were no significant differences in the pain scores between breeds. Intra and inter-rater reliability varied from good (>0.70) to very good (>0.81) for all scales. The criterion validity showed a strong correlation (0.76–0.78) between the numerical rating scale and VAS versus UCAPS and CPS, and between UCAPS and CPS (0.76). The UCAPS and CPS were responsive; all items and total scores increased after surgery. Both scales were specific (81–85%) and sensitive (82–87%). The cut-off point for rescue analgesia was >4 for UCAPS and >3 for CPS. Conclusions. The UCAPS and CPS are valid and reliable to assess postoperative pain in Bos taurus and Bos indicus bulls. Full article
(This article belongs to the Special Issue Advances in Pain Management in Large Animals)
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19 pages, 2292 KiB  
Article
Improving Ovine Behavioral Pain Diagnosis by Implementing Statistical Weightings Based on Logistic Regression and Random Forest Algorithms
by Pedro Henrique Esteves Trindade, João Fernando Serrajordia Rocha de Mello, Nuno Emanuel Oliveira Figueiredo Silva and Stelio Pacca Loureiro Luna
Animals 2022, 12(21), 2940; https://doi.org/10.3390/ani12212940 - 26 Oct 2022
Cited by 9 | Viewed by 2074
Abstract
Recently, the Unesp-Botucatu sheep acute pain scale (USAPS) was created, refined, and psychometrically validated as a tool that offers fast, robust, and simple application. Evidence points to an improvement in pain diagnosis when the importance of the behavioral items of an instrument is [...] Read more.
Recently, the Unesp-Botucatu sheep acute pain scale (USAPS) was created, refined, and psychometrically validated as a tool that offers fast, robust, and simple application. Evidence points to an improvement in pain diagnosis when the importance of the behavioral items of an instrument is statistically weighted; however, this has not yet been investigated in animals. The objective was to investigate whether the implementation of statistical weightings using machine learning algorithms improves the USAPS discriminatory capacity. A behavioral database, previously collected for USAPS validation, of 48 sheep in the perioperative period of laparoscopy was used. A multilevel binomial logistic regression algorithm and a random forest algorithm were used to determine the statistical weights and classify the sheep as to whether they needed analgesia or not. The quality of the classification, estimated by the area under the curve (AUC) and its 95% confidence interval (CI), was compared between the USAPS versions. The USAPS AUCs weighted by multilevel binomial logistic regression (96.59 CI: [95.02–98.15]; p = 0.0004) and random forest algorithms (96.28 CI: [94.17–97.85]; p = 0.0067) were higher than the original USAPS AUC (94.87 CI: [92.94–96.80]). We conclude that the implementation of statistical weights by the two machine learning algorithms improved the USAPS discriminatory ability. Full article
(This article belongs to the Special Issue Advances in Pain Management in Large Animals)
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