A DELPHI STUDY TO BUILD PAIN SCORING SCALE IN CATTLE

Detecting pain in cattle is a prerequisite for good health and welfare management. Until recently, there was no specific pain detection scale for cattle beyond lameness scoring. More recently, some grids have been developed for cattle:some specific to surgery (BOTUCATU grid fromDe Oliveira et al., 2014) and others not specific to a particular disease (Gleerup’s pain scale fromGleerup et al., 2015). In addition, although the use ofanalgesics seems to be increasing, there is a need for greater awareness of the subject among farmers and veterinarians. Delphi method is an
interactiveforecasting method which relies on a panel of experts and can be used to help reach expert consensus and develop professional Therefore, the objective of this study was, through a Delphimethod, to propose simple and if possible specific grids for detecting pain in cattle in thecontext ofspecific diseases.

After a survey ofexisting grids, all the signs of pain in cattle recognised in the literature were listed. The validation and combination of the signs of interest were carried out based on the Delphimethod, using a panel of 12 experts recognised for their expertise in cattle pain. Afirst round of the questionnaire was submitted, based on a list of possible signs for each of the following conditions: mastitis, metritis, reticuloperitonitis, bovine respiratory disorders, dystocia, normalcalving, c-section and dehorning, eye disease, omphalitis, diarrhoea. The experts were asked to answer whether or not thesigns included in the questionnaire wererelevant, and ifso specific, to detect pain. After thefirst round (11 experts’responses), the criteria considered relevant by 10 experts at least were conserved for the second round. Experts also could add other signs not listed. The second round aimed to consolidatetheresults ofthefirst round and investigatetherelevance ofadditionalsigns identified by experts. At this stage,
the criteria considered as relevant by 11 experts out of 12 were conserved for the last round which aimed to prioritize the signs and indicate if painkillers should be used when asinglesignwas detected oracertain number has to be detected. The pain grids were created thanks to the experts’ high level ofapproval for many criteria. Groupings ofsigns by clinicalsimilarity were made to reducethelong lists thatcould be dissuasivefor theevaluators.

For instance, for metritis, therelevant signs finally included in the grid were: discomfort on transrectal palpation ofthe uterus, contracted abdomen, increased lying time, stranguria,and rapid tailmovements.
Forcalf diarrhoea, therelevant signs finally included in the grid were: tightabdomen, tenesmus, tooth grinding, tailflicking, and modification oflying boutsand hind legs position. The different grids foreach conditionwill be presented. Although combining severalsignsand conditionscould increasethe grids’ specificity, the grid’s sensitivity seems to befavoured by theexperts who recommend theadministration of NSAIDsas soon asasign of pain is present: doubtmust benefit theanimal.

The grids thus developed through the Delphi method will serve as daily support for farmers and veterinarians to improve the detection and management of pain in cattle. The further steps consist of developing an application for smartphones and tablets for both vets and farmers to test these grids in thefield and increaseawarenessaround painmanagement in cattle