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Record W4384697644 · doi:10.21423/aabppro20228668

Impact of plane of nutrition and analgesic treatment on wound healing and pain following cautery disbudding in preweaned dairy calves

2023· article· en· W4384697644 on OpenAlexaffabout
Cassandra N. Reedman, T.F. Duffield, T.J. DeVries, K. Lissemore, Sarah Adcock, Cassandra B. Tucker, S. Parson, Charlotte B. Winder

Bibliographic record

VenueTexas A&M University Libraries · 2023
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMedicineWound healingAnalgesicNonsteroidalMalnutritionNociceptionAnesthesiaAcetaminophenSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Nutrition has been reported to be a crucial part of the wound healing process in humans as malnutrition has been well-doc­umented to impede wound healing; however, this has not been evaluated in disbudding wounds in calves. Although it is be­coming more common to feed an increased nutritional plane to young dairy calves, 33% of Canadian producers in a 2015 survey were still feeding calves low levels of milk (< 6 L/d). The objec­tive of this study was to determine the impact of a biologically normal plane of nutrition compared to a limited plane on the primary outcome wound healing, and one dose of nonsteroidal anti-inflammatory drug (NSAID) compared to 2 on the second­ary outcomes: lying behaviour, haptoglobin concentrations, and mechanical nociceptive threshold (MNT) in calves disbud­ded via cautery iron.

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)0.000
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.028
GPT teacher head0.272
Teacher spread0.244 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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Same venueTexas A&M University LibrariesSame topicWound Healing and TreatmentsFrench-language works237,207