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Record W1941539324 · doi:10.1111/vsu.12318

Two‐Stage Rumen Cannulation Technique in Dairy Cows

2015· article· en· W1941539324 on OpenAlexaff
R. Martineau, J. Proulx, Cristiano Côrtes, A.F. Brito, T.F. Duffield

Bibliographic record

VenueVeterinary Surgery · 2015
Typearticle
Languageen
FieldVeterinary
TopicAnimal health and immunology
Canadian institutionsUniversity of GuelphAgriculture and Agri-Food Canada
FundersZoetis
KeywordsRumenCannulaMedicineClampSurgeryDairy cattleAnimal scienceStage (stratigraphy)BiologyFood science

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe a 2-stage rumen cannulation technique for dairy cows. STUDY DESIGN: Case series. ANIMALS: 172 dairy cows from 2 research institutions. METHODS: The 2-stage rumen cannulation technique first exteriorized a rumen segment within a wooden clamp, fixing the clamp to the skin with 6 mattress sutures. After 1 week, the necrotic rumen segment was removed, leaving a rumen fistula in which a 7.5 cm cannula was inserted. This was replaced by a 10 cm cannula a further 1 week later. The surgery took an average of 30 minutes. At least 1 assistant is required for the technique. RESULTS: The overall complication frequency was 7/172 (4%). One cow and 1 heifer aborted less than 10 days after surgery. Two late-pregnant heifers died from peritonitis after insertion of the 7.5 cm cannula because of incomplete adhesion of the rumen to the abdominal wall. The exteriorized rumen segment slipped back in the abdomen in 3 cows but was successfully re-clamped prior to insertion of the 7.5 cm cannula. CONCLUSION: A high success rate was achieved with this 2-stage cannulation technique. Postoperative complications were attributed to delayed adhesion of the rumen, perhaps because of stress-related factors (e.g., transport, mixing with other animals, transition period).

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
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 head0.179
GPT teacher head0.377
Teacher spread0.198 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations14
Published2015
Admission routes1
Has abstractyes

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