Learning Confounds Algometric Assessment of Mechanical Thresholds in Normal Dogs
Bibliographic record
Abstract
OBJECTIVE: To perform algometric readings in normal dogs in a design that would assess possible confounding factors. STUDY DESIGN: Prospective study. ANIMALS: Skeletally mature spayed female, intact male and castrated male retriever or retriever mix dogs without orthopedic or neurologic disease (n = 19). METHODS: Twelve common surgical sites were selected for algometric pressure testing. Threshold response was defined as a conscious recognition of the stimulus, and recorded in Newtons. Sites were tested in the same order, and the testing sequence repeated 3 times on each side of the dog. Dogs were tested in the morning and evening of the same day and was repeated 10-14 days later, allowing 4 separate data collections for each dog. RESULTS: Data were analyzed using ANOVA or ANCOVA. When all the data were included in the analysis, dog (P < .0001), order (P < .0001), site (P < .0001), site order (P = .0217), time (P < .0001), day (P < .0001) and repetition (P < .0001) all significantly affected the algometer readings. When only the first reading for each site was included in the analysis, dog (P < .0001), site (P < .0001) and sex (P < .0001) all significantly affected algometer readings. CONCLUSION: These results suggest that learning occurred over repeated collection time points, with dogs anticipating the stimulus and reacting at lower thresholds.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".