Comparison of measurements obtained by use of an electrogoniometer and a universal plastic goniometer for the assessment of joint motion in dogs
Bibliographic record
Abstract
OBJECTIVE: To compare measurements obtained by use of a universal plastic goniometer (UG) and an electrogoniometer (EG) and from radiographs and to compare joint motion in German Shepherd Dogs and Labrador Retrievers. ANIMALS: 12 healthy adult German Shepherd Dogs and data previously collected from 16 healthy adult Labrador Retrievers. PROCEDURES: German Shepherd Dogs were sedated. One investigator then measured motion of the carpal, cubital (elbow), shoulder, tarsal, stifle, and hip joints of the sedated dogs. Measurements were made in triplicate with a UG and an EG. Radiographs were taken of all joints in maximal flexion and extension. Values were compared between the UG and EG and with values previously determined for joints of 16 Labrador Retrievers. RESULTS: An EG had higher variability than a UG for all dogs. The EG variability appeared to result from the technique for the EG. German Shepherd Dogs had lower values in flexion and extension than did Labrador Retrievers for all joints, except the carpal joints. German Shepherd Dogs had less motion in the tarsal joints, compared with motion for the Labrador Retrievers, but had similar motion in all other joints. CONCLUSIONS AND CLINICAL RELEVANCE: A UG is reliable for obtaining measurements in German Shepherd Dogs. There was higher variability for the EG than for the UG, and an EG cannot be recommended for use.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".