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Record W2789714250 · doi:10.54846/jshap/1011

Shoulder lesions in sows: A review of their causes, prevention, and treatment

2018· review· en· W2789714250 on OpenAlexaff
Fiona C. Rioja-Lang, Yolande M. Seddon, Jennifer Brown

Bibliographic record

VenueJournal of Swine Health and Production · 2018
Typereview
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsGenome PrairieUniversity of Saskatchewan
FundersNational Pork Board
KeywordsMedicineCullingScapulaDebridement (dental)SurgeryHerdVeterinary medicine

Abstract

fetched live from OpenAlex

Severe shoulder lesions in sows are manifested as ulcers comparable to pressure ulcers in humans. In sows, shoulder lesions appear on the skin overlying the bony prominence of the scapula, and are most commonly observed in the first weeks of lactation. Shoulder ulcers arise due to prolonged compression of blood vessels around the tuber of the scapular spine when the sow is lying, leading to insufficient blood circulation, necrosis, and subsequent ulceration. Due to the nature of shoulder lesions and their estimated occurrence (5%-50% of breeding sows worldwide), they represent an obvious welfare concern. There is also an economic impact due to labor time for treatment, medication, and premature culling of sows. While multiple factors contribute to ulcer development, maintaining optimum body condition in sows appears to be a key factor in prevention. This review summarizes the literature on sow shoulder ulcers, including the causes, prevention, and treatment. Regular monitoring of lesions is recommended, as this will help to identify individual farm causes and prevention measures. While much is known about shoulder ulcers, we conclude that there are significant gaps in the scientific literature regarding the mechanisms of development and healing, pain caused, and effective means for treatment and prevention.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.929
Threshold uncertainty score0.552

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.249
GPT teacher head0.476
Teacher spread0.227 · 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 designOther design
Domainnot available
GenreReview

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

Citations29
Published2018
Admission routes1
Has abstractyes

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