MétaCan
Menu
Back to cohort
Record W2238939017

Evaluation of the efficacy of tulathromycin as a metaphylactic antimicrobial in feedlot calves.

2007· article· en· W2238939017 on OpenAlexaff
Booker Cw, Sameeh M. Abutarbush, Schunicht Oc, Jim Gk, Tye Perrett, Brian K Wildman, Guichon Pt, Pittman Tj, C. A. Jones, Pollock Cm

Bibliographic record

VenuePubMed · 2007
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMicrobial infections and disease research
Canadian institutionsAlberta Health Services
Fundersnot available
KeywordsFeedlotTilmicosinAntimicrobialMedicineOxytetracyclineVeterinary medicineAnimal scienceAntibioticsBiologyMicrobiology
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study was to determine the efficacy and cost-effectiveness of tulathromycin (DRAX) versus tilmicosin (MIC) or oxytetracycline (TET) as a metaphylactic antimicrobial in feedlot calves. Calves that received DRAX had significantly (P<.05) lower initial undifferentiated fever (UF) treatment and relapse rates; lower overall chronicity, overall mortality, and cause-specific mortality rates; higher average daily gains; and improved quality grades. However, calves that received DRAX also had poorer (P<.05) yield grades compared with calves that received MIC or TET and worse feed conversion compared with calves that received MIC. Net advantages in the DRAX group were 3.79CanDollars/animal and 16.96CanDollars/animal compared with the MIC and TET groups, respectively. Based on these results, DRAX is a more efficacious and cost-effective metaphylactic antimicrobial than MIC or TET in feedlot calves at ultra-high risk of developing UF. In addition, this study presents a comparison between two methods ("deads out" and "deads in") of calculating feedlot performance variables.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.649
Threshold uncertainty score0.265

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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)0.0000.000

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 head0.042
GPT teacher head0.307
Teacher spread0.266 · 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

Citations50
Published2007
Admission routes1
Has abstractyes

Explore more

Same venuePubMedSame topicMicrobial infections and disease researchFrench-language works237,207