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Effect of tolfenamic acid in postpartum gilts and the performance of their piglets

2023· article· en· W4382771794 on OpenAlexaff
André Maurício Buzato, Aline Kummer, Arlei Coldebella, Jalusa Deon Kich, P. Renaud, R. P. de Carvalho

Bibliographic record

VenueSemina Ciências Agrárias · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsVétoquinol (Canada)
FundersInje University
KeywordsMedicineDiarrheaAnimal scienceLactationWeight gainBody weightInternal medicinePregnancyBiology

Abstract

fetched live from OpenAlex

Postpartum dysgalactia syndrome (PPDS) is a common disorder affecting sows in intensive production systems. In most cases, hypogalactia is not clearly identified and assumes a subclinical aspect. Therefore, the present study aimed to evaluate the effect of a nonsteroidal anti-inflammatory drug (NSAID) based on tolfenamic acid as a prophylactic treatment for PPDS and the performance of suckling piglets. Gilts (n = 319) were randomly divided into two groups: a tolfenamic acid group (n = 157) and a control (n = 162). The tolfenamic acid group received a single intramuscular injection (1 ml/20 kg of 4% tolfenamic acid) after farrowing, whereas the control group received no treatment. The occurrence of PPDS was confirmed. All piglets (n = 4,466) were weighed at 1, 4, and 18 days of age. All litters were evaluated for weight gain, the occurrence of diarrhea, and mortality between 4 and 18 days of age. PPDS variables were analyzed using logistic regression. Piglet weights were analyzed based on covariance while considering the effects of initial weight and the presence of diarrhea. Tolfenamic acid had no significant effect on the incidence of PPDS. The tolfenamic acid group had a 0.41% lower piglet mortality rate until 18 days of age. Tolfenamic acid administered prophylactically to gilts after farrowing reduced piglet mortality during lactation and promoted weight gain.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.878
Threshold uncertainty score0.117

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.212
Teacher spread0.204 · 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 designBench or experimental
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

Citations0
Published2023
Admission routes1
Has abstractyes

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