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Record W2914176090 · doi:10.3168/jds.2018-15129

Intensity of estrus following an estradiol-progesterone-based ovulation synchronization protocol influences fertility outcomes

2019· article· en· W2914176090 on OpenAlexafffund
A.M.L. Madureira, Liam Polsky, T.A. Burnett, B.F. Silper, Sergi Soriano, A.F. Sica, Ky G Pohler, J.L.M. Vasconcelos, R.L.A. Cerri

Bibliographic record

VenueJournal of Dairy Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicReproductive Physiology in Livestock
Canadian institutionsUniversity of British Columbia
FundersAgriculture and Agri-Food CanadaCanadian Dairy CommissionDairy Farmers of Canada
KeywordsEstrous cycleCorpus luteumArtificial inseminationOvulationPregnancyFertilityEndocrinologyAnimal scienceMedicineBlood samplingInternal medicineInseminationAndrologyBiologyOvaryHormonePopulationSperm

Abstract

fetched live from OpenAlex

The objective of this study was to examine the association between increased physical activity at the moment of timed artificial insemination (AI), detected by an automated activity monitor (AAM), and fertility outcomes. This paper also investigated factors affecting estrous expression in general. A total of 1,411 AI events from 1,040 lactating Holstein cows were recorded, averaging 1.3 ± 0.6 (±standard deviation) events per cow. Activity (measured as steps/h) was monitored continuously by a leg-mounted AAM located on the rear leg of the cow. Ovulation was synchronized by a timed AI protocol based on estradiol and progesterone. Ovarian ultrasonography was performed in all cows on d -11 (AI = d 0) and in a subset of cows on d 0 (n = 588) and d 7 (n = 819) to determine the presence of a corpus luteum and follicles. The body condition score (1 to 5 scale) was assessed on d 0 and a blood sample was collected for progesterone measurement on d 7. Using the AAM, an estrus event was determined when the relative increase (RI) in physical activity of the cow exceeded 100% of the baseline activity. The physical activity was classified as strong RI (≥300% RI), moderate RI (100-300% RI), or no estrus (<100% RI). Milk production was measured daily and averaged between d -11 and 0. Pregnancy was diagnosed at 32 and 60 d post-AI and pregnancy losses were calculated. The mean RI at estrus was 328.3 ± 132.1%. Cows with strong RI had greater pregnancy per AI than those with moderate RI and those that did not express estrus (35.1 vs. 27.3 vs. 6.2%). When including only cows that successfully ovulated after timed AI, those that displayed strong intensity RI still had greater pregnancy per AI than those with moderate intensity RI or those that did not express estrus (45.1 vs. 34.8 vs. 6.2%). Cows expressing strong RI at timed AI had greater ovulation rates compared with moderate RI and cows that did not express estrus (94.9 vs. 88.2 vs. 49.5%). Furthermore, pregnancy losses were reduced in cows with strong RI compared with cows expressing moderate RI (13.9 vs. 21.7%). Cows with a strong RI at estrus were more likely to have a corpus luteum at the beginning of the protocol and had greater concentration of progesterone 7 d post-AI. Multiparous cows expressed lower RI compared with primiparous cows. Cows with lower body condition score tended to have decreased RI at estrus. No correlation between estrous expression and pre-ovulatory follicle diameter was observed. Also, no correlation was observed between milk production at AI and RI. In conclusion, strong intensity RI of estrus events at timed AI was associated with improved ovulation rates and pregnancy per AI, and reduced pregnancy losses. These results provide further evidence that measurements of estrous expression can be used to predict fertility at the time of AI and possibly be used as a tool to assist decision making strategies of reproduction programs.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.466
Threshold uncertainty score0.185

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.029
GPT teacher head0.297
Teacher spread0.268 · 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 teacher head, 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

Citations63
Published2019
Admission routes2
Has abstractyes

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