Diagnostische Wertigkeit von Computertomographie und Magnetresonanztomographie für die Diagnose einer Koronoiderkrankung beim Hund
Bibliographic record
Abstract
OBJECTIVE: The diagnostic value of CT and MRI regarding the diagnosis of coronoid pathology in the dog. MATERIAL AND METHODS: Computed tomography (CT) and magnetic resonance imaging (MRI) of the elbow joint were performed in dogs with clinical and radiological signs of coronoid pathology. Afterwards, all dogs underwent arthroscopic surgery. For the computed tomographic examination, a 16-slice-CT-scanner spiral-CT (Philips Brilliance 16) was used. The MRI-examination was performed with a 1-Tesla superconducting magnet (Phillips Intera 1.0). T1 and T2 weighted images with different sequences were acquired. RESULTS: In total, 44 elbow joints from 44 patients (total of 12 breeds, including mixed breeds) were examined. The most represented breeds were Labrador Retrievers (38.6%, n=17), mixed breed dogs (22.7%, n=10) and Golden Retrievers (11.4%, n=5) were represented most. The age of the 30 male dogs (68%) and 14 female dogs (32%) ranged from 6 to 117 months (mean 2.25 years). Using CT, the following results could be evaluated: a) fissure at the level of the Processus coronoideus medialis ulnae (PCM) in 66% (n=29); b) fragments at the level of the PCM in 55% (n=24); c) deformation at the level of the PCM in all 44 joints; d) increased opacity at the level of the base of the PCM in all 44 joints; e) heterogenous opacity at the apex of the PCM in 91% (n=41). With MRI, the following results could be evaluated: a) fissure at the level of the PCM in 59% (n=26); b) fragments at the level of the PCM in 57% (n=25); c) deformation at the level of the PCM in 86% (n=38); d) increased opacity at the level of the base of the PCM, thus making assessment impossible; e) heterogenous opacity at the apex of the PCM, thus making assessment impossible. CONCLUSION AND CLINICAL RELEVANCE: Both diganostic imaging modalities are appropriate for evaluating coronoid pathology in the dog.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 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".