BONE SCINTIGRAPHY FOR THE DIAGNOSIS OF AN ABNORMAL MEDIAL CORONOID PROCESS IN DOGS
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".