Evidence map›Paper›PMID 38914736›Full record

ArticleScientific reports2024

Establishment and risk factor assessment of the abnormal body temperature probability prediction model (ABTP) for dairy cattle.

Tsair-Fwu Lee, Chien-Liang Chiu, Yen-Hsien Liu, Chu-Ho Chang, Jen-Chung Shao, Shih-Sian Guo, Yi-Lun Liao, Chia-Hui Chen, Chin-Dar Tseng, Pei-Ju Chao and 1 more

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In one paragraph

Article in Scientific reports, 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

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

1 citing paper in PubMed.

  1. Review
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

11 authors.

Tsair-Fwu LeeDepartment of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan.
Chien-Liang ChiuDepartment of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan.
Yen-Hsien LiuDepartment of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan.
Chu-Ho ChangDepartment of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan.
Jen-Chung ShaoDepartment of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan.
Shih-Sian GuoDepartment of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan.
Yi-Lun LiaoDepartment of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan.
Chia-Hui ChenDepartment of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan.
Chin-Dar TsengDepartment of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan.
Pei-Ju ChaoDepartment of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan. pjchao99@gmail.com.
Shen-Hao LeeDepartment of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan. leeshenhao@gmail.com.

Funding

Ministry of Science and Technology 111-2221-E-992-016-MY2
6 · The paper itself

Abstract

The study aims to develop an abnormal body temperature probability (ABTP) model for dairy cattle, utilizing environmental and physiological data. This model is designed to enhance the management of heat stress impacts, providing an early warning system for farm managers to improve dairy cattle welfare and farm productivity in response to climate change. The study employs the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm to analyze environmental and physiological data from 320 dairy cattle, identifying key factors influencing body temperature anomalies. This method supports the development of various models, including the Lyman Kutcher-Burman (LKB), Logistic, Schultheiss, and Poisson models, which are evaluated for their ability to predict abnormal body temperatures in dairy cattle effectively. The study successfully validated multiple models to predict abnormal body temperatures in dairy cattle, with a focus on the temperature-humidity index (THI) as a critical determinant. These models, including LKB, Logistic, Schultheiss, and Poisson, demonstrated high accuracy, as measured by the AUC and other performance metrics such as the Brier score and Hosmer-Lemeshow (HL) test. The results highlight the robustness of the models in capturing the nuances of heat stress impacts on dairy cattle. The research develops innovative models for managing heat stress in dairy cattle, effectively enhancing detection and intervention strategies. By integrating advanced technologies and novel predictive models, the study offers effective measures for early detection and management of abnormal body temperatures, improving cattle welfare and farm productivity in changing climatic conditions. This approach highlights the importance of using multiple models to accurately predict and address heat stress in livestock, making significant contributions to enhancing farm management practices.

Indexed as

Body TemperatureDairyingAnimalsCattleCattle DiseasesClimate ChangeFemaleHeat Stress DisordersProbabilityRisk AssessmentRisk FactorsAbnormal body temperature probability (ABTP)Dairy cattleLeast absolute shrinkage and selection operator (LASSO)Temperature-humidity index (THI)

Identifiers

PMID38914736
PMCPMC11196662

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LicenceCC BY
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Registered trials

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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.