Functional linear modeling of activity data shows analgesic-mediated improved sleep in dogs with spontaneous osteoarthritis pain
Bibliographic record
Abstract
In humans, pain due to osteoarthritis has been demonstrated to be associated with insomnia and sleep disturbances that affect perception of pain, productivity, and quality of life. Dogs, which develop spontaneous osteoarthritis and represent an increasingly used model for human osteoarthritis, would be expected to show similar sleep disturbances. Further, these sleep disturbances should be mitigated by analgesic therapy. Previous efforts to quantify sleep in osteoarthritic dogs using accelerometry have not demonstrated a beneficial effect of analgesic therapy; this is despite owner-reported improvements in dogs' sleep quality. However, analytic techniques for time-series accelerometry data have advanced with the development of functional linear modeling. Our aim was to apply functional linear modeling to accelerometry data from osteoarthritic dogs participating in a cross-over non-steroidal anti-inflammatory (meloxicam) drug trial. Significant differences in activity patterns were seen dogs receiving drug (meloxicam) vs. placebo, suggestive of improved nighttime resting (sleep) and increased daytime activity. These results align with owner-reported outcome assessments of sleep quality and further support dogs as an important translational model with benefits for both veterinary and human health.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".