Influence of Changes in Body Weight on Peak Vertical Force in Osteoarthritic Dogs: A Possible Bias in Study Outcome
Bibliographic record
Abstract
OBJECTIVE: Force platform gait analysis is a recognized clinical evaluation tool that captures and documents the in vivo pathomechanics of osteoarthritis (OA). In a clinical trial designed to evaluate the impact of 2 specific diets, an increase in body weight (BW) was observed in lame client-owned dogs. Covariance analysis was used to evaluate the interference of BW changes toward the evolution of peak vertical force (PVF) values. These secondary findings are reported in this study. STUDY DESIGN: Prospective study. ANIMALS: Lame dogs (n=26). METHODS: Dogs with radiographic evidence of OA and low PVF values were fed with 2 specific diets for 30 and 60 days. PVF and BW were recorded at baseline, day 30 (D30), and D90. RESULTS: Mean (+/-SD) PVF values (%BW) did not differ significantly over time (D0: 63.9+/-17.2; D30: 65.5+/-17.4; and D90: 66.5+/-20.1). In contrast, BW (kg) was significantly higher at D90 (41.3+/-7.9) when compared with D30 (39.9+/-8.4) and D0 (40.0+/-8.7). Upon covariance analyses, BW changes interfere significantly with PVF values already normalized in %BW (P=.013). Values of PVF adjusted using BW as a covariate were then 63.4+/-17.1 (D0), 65.0+/-17.3 (D30), and 67.6+/-20.5 (D90), whereas D90 was significantly higher than D0. CONCLUSION: These findings highlighted the interference of changes in BW toward locomotor function of OA dogs when using PVF values normalized in %BW. Exacerbation of lameness when a gain in BW occurred was also sustained, raising a possible bias in clinical study outcomes. CLINICAL RELEVANCE: A BW increase in dogs with OA could exacerbate a preexisting lameness and induce a bias in clinical trials.
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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 | 0.038 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".