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Record W4388751004 · doi:10.3168/jdsc.2023-0420

Early pain experiences in dairy calves on pain sensitivity later in life

2023· article· en· W4388751004 on OpenAlexaff
Zimbábwe Osório-Santos, Thomas Ede, María José Hötzel, Daniel M. Weary, M.A.G. von Keyserlingk

Bibliographic record

VenueJDS Communications · 2023
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineNociceptionRumpAcute painAnesthesiaInternal medicineAnatomy

Abstract

fetched live from OpenAlex

In humans, early painful experiences can increase pain sensitivity later in life, but little is known regarding this phenomenon in cattle. This study assessed if a painful event early in life affects later pain sensitivity in 40-d-old calves. Holstein calves (n = 26) were randomly assigned to control or treatment conditions, blocked within sex and birth weight. At 9.5 ± 1.8 d old, treatment calves had 1 horn bud disbudded using caustic paste, whereas control calves had 1 horn sham disbudded; in both cases, multimodal pain control was provided. All calves had the contralateral horn bud disbudded using a hot iron 4 wk later, again with multimodal pain control. Mechanical nociceptive responses were assessed weekly using an algometer applied adjacent to both horn buds and on the rump, beginning 3 d before the first disbudding and ending 30 d after the second disbudding. Following the second disbudding, both groups of calves showed evidence of increased sensitivity (i.e., algometer pressure declined 3.69 ± 0.60 √N to 2.13 ± 0.70 √N) on the contralateral bud, but there was no difference between the control group and the treatment groups. An interaction between treatment and time, likely driven by treatment differences, was found on the rump when tested 5 h after the second disbudding event. These responses are not consistent with the hypothesis that an early pain experience results in increased sensitivity to later painful experiences.

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)0.005
metaresearch head score (Gemma)0.002
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 score0.047
Threshold uncertainty score0.416

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.054
GPT teacher head0.332
Teacher spread0.278 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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