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Record W2163311359 · doi:10.2460/javma.229.1.92

Perioperative administration of antimicrobials associated with elective surgery for cranial cruciate ligament rupture in dogs: 83 cases (2003–2005)

2006· article· en· W2163311359 on OpenAlexaff
J. Scott Weese, Krista B. Halling

Bibliographic record

VenueJournal of the American Veterinary Medical Association · 2006
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMedicinePerioperativeAnesthesiaCruciate ligamentSurgeryAntimicrobialAnterior cruciate ligament

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate perioperative antimicrobial use associated with elective surgery for cranial cruciate ligament rupture in dogs. DESIGN: Retrospective case series. ANIMALS: 83 dogs. PROCEDURES: Medical records were reviewed and antimicrobial use practices were evaluated for dogs with no other problems that would affect antimicrobial use decisions. RESULTS: Antimicrobials were administered before or during surgery to 75 of 83 (90%) dogs. Timing of administration with respect to first incision, intraoperative administration, and duration of administration were variable. Antimicrobial administration began after surgery in 3 (3.6%) dogs. Fifty-five of 65 (85%) dogs treated before surgery received the first dose within 60 minutes of the first incision. Time from first antimicrobial administration to closure of the incision ranged from 15 to 285 minutes (mean +/- SD, 141 +/- 53 minutes). If a guideline of repeated administration every 2 hours after first administration until closure of the surgical site was used, 22 of 43 (51%) dogs received the required intraoperative dose, whereas 6 of 32 (19%) dogs that did not require intraoperative treatment were treated. Twenty-four (29%) dogs received antimicrobials after surgery. CONCLUSIONS AND CLINICAL RELEVANCE: Certain discrepancies between antimicrobial use practices in this study and standard guidelines used in human medicine were evident.

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)0.003
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 score0.453
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.317
Teacher spread0.284 · 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

Citations50
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American Veterinary Medical AssociationSame topicVeterinary Orthopedics and NeurologyFrench-language works237,207