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Record W4405437717 · doi:10.3168/jds.2024-25413

Effect of nonsteroidal anti-inflammatory drugs on neonatal calf diarrhea when administered at a disease alert generated by automated milk feeders

2024· article· en· W4405437717 on OpenAlexafffund
Allison Welk, M.C. Cantor, Heather W. Neave, J.H.C. Costa, Jannelle Morrison, Charlotte B. Winder, D.L. Renaud

Bibliographic record

VenueJournal of Dairy Science · 2024
Typearticle
Languageen
FieldVeterinary
TopicAnimal health and immunology
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaDairy Farmers of OntarioOntario Research Foundation
KeywordsNonsteroidalMedicineDiarrheaDiseasePharmacologyInternal medicine

Abstract

fetched live from OpenAlex

The objective of this randomized clinical trial was to assess whether early intervention with a nonsteroidal anti-inflammatory drug (NSAID) following a disease alert generated by automated milk feeders could reduce diarrhea severity and improve performance in dairy calves. A total of 71 Holstein calves were enrolled on an automated milk feeder (recorded milk intake and drinking speed) at 3 d of age and received up to 15 L/d (150 g/L) of milk replacer until 35 d of age. An alert that was previously validated as diagnostically accurate to identify calves at risk for diarrhea was used using automated milk feeder data (≤60% rolling dividends in milk intake or drinking speed over 2 d). At their first alert, calves were randomly allocated to receive a single subcutaneous injection of meloxicam (Metacam, Boehringer Ingelheim) at a rate of 0.5 mg/kg of BW (NSAID) or an equal volume of saline as a placebo control (CON). Fecal consistency was scored daily, and calves were diagnosed with diarrhea when they had loose feces for ≥2 d or watery feces for ≥1 d. Body weight was recorded at birth and weekly thereafter. A subset of calves (n = 32) were fitted with IceQube pedometers at 3 d of age to measure activity behaviors (lying time and step count). Mixed linear regression models were used to assess the association of study treatment with the duration of diarrhea after the alert and to evaluate the association of study treatment with milk intake, drinking speed, lying time, overall activity for 5 d following the alert, and ADG for 3 wk following the alert. On average, calves triggered an alert at (mean ± SD) 9.3 ± 2.3 d of age and were diagnosed with diarrhea at 9.6 ± 2.1 d of age. Diarrhea duration was similar between treatments (NSAID: 2.85 vs. CON: 2.94 ± 0.37 d), as were feeding behaviors (milk intake [NSAID: 8.2 vs. CON: 8.1 ± 0.4 L/d] and drinking speed [NSAID: 0.38 vs. CON: 0.37 ± 0.02 min/L]). Treatment was also not associated with ADG for the 3 wk after the alert (NSAID: 0.97 vs. CON: 0.97 ± 0.06 kg/d). However, calves provided an NSAID had reduced odds of being treated with electrolytes (odds ratio = 0.32, 95% CI: 0.10-0.98). In addition, calves provided an NSAID spent less time lying (NSAID: 17.64 vs. CON: 18.17 ± 0.19 h/day) and performed more steps over the 5 d following the alert (NSAID: 789.1 vs. CON: 628.0 steps/d), suggesting that CON calves may have been more lethargic. Overall, providing an NSAID at the time of a diarrhea alert did not affect diarrhea duration, feed intake, or growth. However, providing an NSAID increased activity in the 5 d following the alert, which may have reduced pain and symptoms of lethargy, indicating a milder response to the disease. We suggest that providing an NSAID at the time of diarrhea alert had little benefit on the calf; however, further work is needed to understand behaviors associated with malaise and pain in calves with diarrhea as well as the efficacy of NSAID under different management conditions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.318
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2024
Admission routes2
Has abstractyes

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