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Record W2515003449 · doi:10.54846/jshap/749

Investigation of the use of meloxicam post farrowing for improving sow performance and reducing pain

2014· article· en· W2515003449 on OpenAlexaff
Ryan Tenbergen, Robert Friendship, Glen Cassar, Manuel Amezcua, Derek B. Haley

Bibliographic record

VenueJournal of Swine Health and Production · 2014
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMeloxicamMedicineAnimal scienceAnesthesiaBiology

Abstract

fetched live from OpenAlex

Objectives: To determine the effects of meloxicam administered to sows shortly after parturition on nursing behaviour and piglet survival and growth. Materials and methods: A total of 289 sows and their litters were used. Sows within 12 hours of farrowing were randomly allocated to receive either an intramuscular injection of meloxicam (extra-label) or a placebo. Researchers were blinded to treatment. All piglets were weighed within 12 hours of birth, at castration and tail-docking (5 to 7 days of age), and prior to weaning (19 to 21 days of age). Litters were categorized as small, medium, and large. Additional measurements involving the sow, including position changes, rectal temperatures, and feed-intake scores, were performed on a smaller number of the study sows. Results: There were no significant treatment effects on piglet mortality or growth rate. However, growth rate of pigs in medium-sized litters (11 to 13 pigs) tended to be better for sows treated with meloxicam than for sows given a placebo (P = .07). Growth rate was positively correlated with weight at birth and at weaning (P < .001) and negatively correlated with sow parity and litter size at birth (P < .001). Piglet mortality was not associated with treatment, but was associated with large litter size and light birth weight (P < .001). Implications: Meloxicam given to all sows post farrowing does not result in improved piglet survival and growth. Improved performance might be noted if only sows having difficult farrowings were treated. Further studies are required to confirm.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.793
Threshold uncertainty score0.159

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.097
GPT teacher head0.308
Teacher spread0.211 · 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

Citations19
Published2014
Admission routes1
Has abstractyes

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