Evidence map›Paper›PMID 39619920›Full record

ArticleVeterinary world2024

Prediction model for rectal temperature in cats with different baseline characteristics using a non-contact infrared thermometer.

Nattakarn Naimon, Thitichai Jarudecha, Metita Sussadee, Rattana Muikaew, Supochana Charoensin

Abstract read
In one paragraph

Article in Veterinary world, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. 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

5 authors.

Nattakarn NaimonDepartment of Veterinary Nursing, Faculty of Veterinary Technology, Kasetsart University, 50 Ngam Wong Wan Road, Ladyaow, Chatuchak, Bangkok, Thailand.
Thitichai JarudechaDepartment of Veterinary Nursing, Faculty of Veterinary Technology, Kasetsart University, 50 Ngam Wong Wan Road, Ladyaow, Chatuchak, Bangkok, Thailand.
Metita SussadeeDepartment of Veterinary Nursing, Faculty of Veterinary Technology, Kasetsart University, 50 Ngam Wong Wan Road, Ladyaow, Chatuchak, Bangkok, Thailand.
Rattana MuikaewDepartment of Veterinary Nursing, Faculty of Veterinary Technology, Kasetsart University, 50 Ngam Wong Wan Road, Ladyaow, Chatuchak, Bangkok, Thailand.
Supochana CharoensinDepartment of Veterinary Technology, Faculty of Veterinary Technology, Kasetsart University, 50 Ngam Wong Wan Road, Ladyaow, Chatuchak, Bangkok, Thailand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Aim: Body temperature is the most useful clinical parameter for evaluating animal health. In clinical practice, rectal temperature is the gold standard for assessing body temperature, but rectal temperature measurement is not convenient and can cause stress in animals. The non-contact infrared thermometer is considered an alternative method for skin temperature measurements in animals. Many biological factors may influence the response of body regions to thermal challenges; thus, the identification of these variables is essential for accurate infrared temperature measurements. This study aimed to estimate the relationship between the physiological factors of cats and their body temperature measured across various body positions, as well as to propose a model for predicting rectal temperature using an infrared thermometer. Materials and Methods: A total of 184 client-owned cats were included in this study. The infrared temperature (°F) was measured using a non-contact infrared thermometer at five body positions: maxillary canine gingival margin (GCT), anal skin (ANS), inguinal canal (ING), ear canal (EC), and palmar pad. The five biological factors (age, body condition score [BCS], gender, hair type, and hair color) were recorded and analyzed to adjust predictive factors for rectal temperature prediction. All statistical analyses were performed using multivariable linear regression. The rectal temperature prediction model was then designed using the forward stepwise selection method. Results: Based on multivariable linear regression analysis of infrared temperature results, the pre-prediction model showed significant correlations with rectal temperature for ANS, GCT, and EC (p = 0.0074, 0.0042, and 0.0118, respectively). Moreover, the combination of infrared temperatures on ANS and ING was the most appropriate parameter for predicting rectal temperature (p = 0.0008). All models were adjusted according to the baseline characteristics of the cats. However, the adjusted R-squared values of the pre-prediction model of the infrared temperature on the ANS, GCT, and EC and the final prediction model by the infrared temperature on the ANS combined with the ING were low (8.7%, 8.9%, 7.3%, and 12.8%, respectively). Conclusion: The prediction model of rectal temperature of cats by infrared temperature from a non-contact infrared thermometer in ANS combined with ING and adjusted by age, BCS, hair type, and hair color may be applicable for use in clinical practice. This study found that the adjusted R-squared values of all models were low; the predictive model will need to be developed and used to test validity and reliability with an external study group for assessing their practical usefulness.

Indexed as

catnoncontact infrared thermometerprediction modelrectal temperature

Identifiers

PMID39619920
PMCPMC11606290

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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.