Evidence map›Paper›PMID 32182701›Full record

ArticleSensors (Basel, Switzerland)2020

Combination of Sensor Data and Health Monitoring for Early Detection of Subclinical Ketosis in Dairy Cows.

Valentin Sturm, Dmitry Efrosinin, Manfred Öhlschuster, Erika Gusterer, Marc Drillich, Michael Iwersen

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
2.7field-weighted citation impact, top 10% 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

15 citing papers in PubMed, 29 citations in OpenAlex.

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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 3 institutions in 1 country.

Valentin SturmLinz Center of Mechatronics GmbH, 4040 Linz, Austria.
Dmitry EfrosininInstitute of Stochastics, Johannes Kepler University Linz, 4040 Linz, Austria.ORCID 0000-0002-0902-6640
Manfred ÖhlschusterSMARTBOW GmbH, 4675 Weibern, Austria.
Erika GustererClinical Unit for Herd Health Management in Ruminants, University Clinic for Ruminants, Department for Farm Animals and Veterinary Public Health, University of Veterinary Medicine Vienna, 1210 Vienna, Austria.
Marc DrillichClinical Unit for Herd Health Management in Ruminants, University Clinic for Ruminants, Department for Farm Animals and Veterinary Public Health, University of Veterinary Medicine Vienna, 1210 Vienna, Austria.ORCID 0000-0002-2824-8185
Michael IwersenClinical Unit for Herd Health Management in Ruminants, University Clinic for Ruminants, Department for Farm Animals and Veterinary Public Health, University of Veterinary Medicine Vienna, 1210 Vienna, Austria.ORCID 0000-0001-7893-6050
University of Veterinary Medicine Vienna · ATJohannes Kepler University of Linz · ATLinz Center of Mechatronics (Austria) · AT

Funding

Österreichische Forschungsförderungsgesellschaft 848610
6 · The paper itself

Abstract

Subclinical ketosis is a metabolic disease in early lactation. It contributes to economic losses because of reduced milk yield and may promote the development of secondary diseases. Thus, an early detection seems desirable as it enables the farmer to initiate countermeasures. To support early detection, we examine different types of data recordings and use them to build a flexible algorithm that predicts the occurence of subclinical ketosis. This approach shows promising results and can be seen as a step toward automatic health monitoring in farm animals.

Indexed as

KetosisAlgorithmsAnimalsCattleDairyingDiagnosis, Computer-AssistedFemaleLactationMachine LearningMonitoring, Physiologicketosismachine learningprecision dairy farmingsensor fusiontime series classification

Identifiers

PMID32182701
PMCPMC7085771
OpenAlexW3009641158

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.