Evidence map›Paper›PMID 34649629›Full record

ArticlePorcine health management2021

Adding value to food chain information: using data on pig welfare and antimicrobial use on-farm to predict meat inspection outcomes.

Joana Pessoa, Conor McAloon, Maria Rodrigues da Costa, Edgar García Manzanilla, Tomas Norton, Laura Boyle

Open access · goldAbstract read
In one paragraph

Article in Porcine health management, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.9field-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

5 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
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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

6 authors at 4 institutions in 4 countries.

Joana PessoaPig Development Department, Teagasc Animal and Grassland Research and Innovation Centre, Moorepark, Ireland. joana.pessoa@teagasc.ie.ORCID http://orcid.org/0000-0002-8959-9333
Conor McAloonSection of Herd Health and Animal Husbandry, School of Veterinary Medicine, University College Dublin, Belfield, Ireland.
Maria Rodrigues da CostaEpidemiology Research Unit, Department of Veterinary and Animal Science, Northern Faculty, Scotland's Rural College (SRUC), Inverness, Scotland.
Edgar García ManzanillaPig Development Department, Teagasc Animal and Grassland Research and Innovation Centre, Moorepark, Ireland.
Tomas NortonM3-BIORES-Measure, Model and Manage Bioresponses, KU Leuven, Leuven, Belgium.
Laura BoylePig Development Department, Teagasc Animal and Grassland Research and Innovation Centre, Moorepark, Ireland.
University College Dublin · IEKU Leuven · BEScotland's Rural College · GBTeagasc - The Irish Agriculture and Food Development Authority · IE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundUsing Food Chain Information data to objectively identify high-risk animals entering abattoirs can represent an important step forward towards improving on-farm animal welfare. We aimed to develop and evaluate the performance of classification models, using Gradient Boosting Machine algorithms that utilise accurate longitudinal on-farm data on pig health and welfare to predict condemnations, pluck lesions and low cold carcass weight at slaughter.

resultsThe accuracy of the models was assessed using the area under the receiver operating characteristics (ROC) curve (AUC). The AUC for the prediction models for pneumonia, dorsocaudal pleurisy, cranial pleurisy, pericarditis, partial and total condemnations, and low cold carcass weight varied from 0.54 for pneumonia and 0.67 for low cold carcass weight. For dorsocaudal pleurisy, ear lesions assessed on pigs aged 12 weeks and antimicrobial treatments (AMT) were the most important prediction variables. Similarly, the most important variable for the prediction of cranial pleurisy was the number of AMT. In the case of pericarditis, ear lesions assessed both at week 12 and 14 were the most important variables and accounted for 33% of the Bernoulli loss reduction. For predicting partial and total condemnations, the presence of hernias on week 18 and lameness on week 12 accounted for 27% and 14% of the Bernoulli loss reduction, respectively. Finally, AMT (37%) and ear lesions assessed on week 12 (15%) were the most important variables for predicting pigs with low cold carcass weight.

conclusionsThe findings from our study show that on farm assessments of animal-based welfare outcomes and information on antimicrobial treatments have a modest predictive power in relation to the different meat inspection outcomes assessed. New research following the same group of pigs longitudinally from a larger number of farms supplying different slaughterhouses is required to confirm that on farm assessments can add value to Food Chain Information reports.

Indexed as

Boosted treesFood chain informationMachine learningMeat inspectionPigWelfare

Identifiers

PMID34649629
PMCPMC8518164
OpenAlexW3206494383

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.