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Record W4412798754 · doi:10.1055/a-2655-9218

Incidence of Bandage-Associated Complications in Cats following Clean Orthopaedic Procedures: A Retrospective Study of 152 Cases

2025· article· en· W4412798754 on OpenAlexaff
S. Costello, Mélanie Olive, Andrew S. Levien, QiCai Jason Hoon, Alen Lai, King Mac, Evelyn Hall, Rachel M. Basa

Bibliographic record

VenueVeterinary and Comparative Orthopaedics and Traumatology · 2025
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Surgery and Rehabilitation
Canadian institutionsToronto East General Hospital
Fundersnot available
KeywordsMedicineBandageCATSComplicationSurgeryIncidence (geometry)Retrospective cohort studySplintsMedical recordSplint (medicine)Orthopedic surgeryPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

To report the incidence of bandage-associated complications in cats following clean orthopaedic injury.Multi-institutional retrospective case series of 152 client-owned cats. Medical records were searched for cats that had a bandage placed after a clean orthopaedic injury. Data collected included: signalment, diagnosis, anatomical region, orthopaedic procedure, professional role of the person applying the bandage (specialist/resident/nurse), bandage duration, complications, and outcomes.A total of 152 cats had bandages placed after clean orthopaedic injuries. Complications were reported in 104 cats (68.4%). Bandage-related complications were mild in 64.4% cases, moderate in 32.7% cases, and severe 2.9% of cases. If a cat had a splint placed, it was 3.4 times more likely to have a more severe complication compared with a cat which did not require a splint.Bandage complications occur frequently in cats, and the use of splints was a significant predictor of increased complication severity. Clinicians should be particularly vigilant when bandaging limbs in cats, as complications secondary to bandaging occur frequently. These findings underscore the importance of appropriate case selection for bandage application and monitoring strategies to minimize the risk of complications.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.591

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.053
GPT teacher head0.349
Teacher spread0.296 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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