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Record W3127665748 · doi:10.1055/s-0040-1722336

Establishment of Normal Mechanical Tibial Joint Angles in Dachshunds

2021· article· en· W3127665748 on OpenAlexaff
Charlotte Banks, Richard Meeson, Elvin Kulendra, Darren Carwardine, Benjamin Mielke, Matthew J. Pead, Helen R. P. Phillips, Andrew Phillips

Bibliographic record

VenueVeterinary and Comparative Orthopaedics and Traumatology · 2021
Typearticle
Languageen
FieldMedicine
TopicFoot and Ankle Surgery
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsSagittal planeMedicineCoronal planeOrthodonticsRadiographyIntraclass correlationAnatomySurgery

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this study was to establish breed-standard mechanical tibial joint reference angles in the frontal plane in Dachshunds. STUDY DESIGN: = 32) radiographs of normal tibiae from Dachshunds were retrospectively reviewed. The mechanical medial proximal, mechanical medial distal, mechanical caudal proximal and mechanical cranial distal tibial angles were measured on three occasions by two separate observers using previously established methodology. Interclass correlation coefficient was used to assess the reliability of radiographic measurements. RESULTS: The mean and standard deviation for mechanical medial proximal, mechanical medial distal, mechanical caudal proximal and mechanical cranial distal were 93.1 degrees ± 4.2, 97.5 degrees ± 3.9, 75.3 degrees ± 3.7 and 85.0 degrees ± 5.3 respectively. Intra-observer reliability was good to excellent for all measures, while inter-observer reliability was moderate to excellent in the frontal plane and poor to good in the sagittal plane. Dachshund-specific joint reference angles were similar to a range of previously reported non-chondrodystrophic breeds in the frontal plane but differed to most in the sagittal plane. CONCLUSION: Dachshund tibial joint reference angles are reported which can be used in surgical planning for correction of bilateral pes varus.

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 score0.189
Threshold uncertainty score0.447

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.102
GPT teacher head0.309
Teacher spread0.207 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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