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BONE SCINTIGRAPHY FOR THE DIAGNOSIS OF AN ABNORMAL MEDIAL CORONOID PROCESS IN DOGS

2010· article· en· W1533399388 on OpenAlexaboutno aff
Leonie W. L. van Bruggen, Herman A.W. Hazewinkel, Claudia F. Wolschrijn, George Voorhout, Yvonne W.E.A. Pollak, Paul Y. Barthez

Bibliographic record

VenueVeterinary Radiology & Ultrasound · 2010
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLamenessElbowRadiographyScintigraphyBone scintigraphyRadiologyNuclear medicine

Abstract

fetched live from OpenAlex

Few reports have been published regarding the use of scintigraphy in the diagnosis of elbow joint lameness in dogs. Some authors have speculated about the potential use of bone scintigraphy and its suspected high sensitivity for the early diagnosis of abnormalities of the medial coronoid process (MCP) in dogs. Scintigraphy is used routinely in our institution in dogs presented for thoracic limb lameness and/or suspected of abnormalities of the MCP when radiographic findings were equivocal. Radiographic, scintigraphic, and surgical findings of the elbow joints of 17 dogs with elbow joint lameness were compared with radiographic, scintigraphic, and necropsy findings of the elbow joints of 12 clinically healthy Labrador Retrievers. Quantitative evaluation of scintigraphic images was performed to determine relative radiopharmaceutical uptake in the region of the MCP. Maximum relative uptake of the coronoid process in the normal dogs was taken as a threshold value to classify elbows as positive or negative for an abnormal MCP after all 24 elbows of the 12 healthy dogs were confirmed as being normal at necropsy. All 17 elbows from lame dogs were positive on scintigraphy and confirmed as having chondromalacia, a fissure, or fragmentation of the MCP. Based on our results, bone scintigraphy may be a valuable diagnostic tool for the diagnosis of abnormalities of the MCP in dogs, and particularly in older dogs where clinical and radiographic changes may be ambiguous.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.625
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.041
GPT teacher head0.329
Teacher spread0.289 · 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.

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

Citations17
Published2010
Admission routes1
Has abstractyes

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