MétaCan
Menu
Back to cohort
Record W2972029693 · doi:10.1055/s-0039-1695749

Sub-Bandage Pressure in the Canine Forelimb after Rigid Splint Application by Surgeons and Veterinary Students

2019· article· en· W2972029693 on OpenAlexaboutno aff
Molly A. Vitt, Daniel C. Wingert, Michael G. Conzemius

Bibliographic record

VenueVCOT Open · 2019
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsnot available
FundersTata SonsUniversity of Minnesota
KeywordsBandageSplint (medicine)MedicineForelimbSurgeryCompression BandageOrthodonticsAnatomy

Abstract

fetched live from OpenAlex

Objective The aim of this study was to measure and compare sub-bandage pressures after a rigid splint was applied to the forelimb of a dog by surgeons and veterinary students. Animals One, adult, Labrador Retriever. Methods Sub-bandage pressure was measured at five locations on the limb of a dog using a previously validated pneumatic compression measurement system over a 4-hour period after splint application. All participants received the same instructions and the same dog was used for each splint application. Results Across time and location, mean sub-bandage pressures from the experienced group were significantly greater than those from the inexperienced group at all transducer locations and at all time points. People from the inexperienced group recorded the greatest range in sub-bandage pressures and had significantly higher-pressure differences across the five locations sub-bandage pressure was measured. Conclusions and Clinical Relevance Surgeons applied their splint bandages with approximately 50% greater pressure and 50% less variability between locations. The large range in sub-bandage pressures found may suggest that decreased and/or increased sub-bandage pressure may predispose to bandage 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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.368
Threshold uncertainty score0.581

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.001
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 head0.028
GPT teacher head0.337
Teacher spread0.309 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueVCOT OpenSame topicVeterinary Orthopedics and NeurologyFrench-language works237,207