Quantitative Sensory Testing in Animal Models of Chronic Pain: A Pilot Study
Bibliographic record
Abstract
Introduction: Quantitative sensory testing (QST) evaluates the patient’s somatosensory profile. This pilot study aimed to compare the sensory sensitivity of healthy and affected dogs with chronic pain condition. Materials and Methods: Static and dynamic QST included punctate tactile and mechanical threshold, and conditioned pain modulation (CPM) delta. Healthy client-owned dogs ( n = 7) were evaluated twice in a day, 4 hours apart. Data from healthy dogs were compared with laboratory dogs before and after (9 weeks) surgically-induced osteoarthritis (OA) ( n = 6) and client-owned dogs with natural osteosarcoma (OSA) ( n = 8). Inferential statistics were done at 5% α-threshold, data are mean(SD). Results: In healthy dogs, intra-class correlation coefficients for tactile and mechanical thresholds at the primary site were 0.96 and 0.36, respectively; and 0.89 for mechanical threshold at a distal site. A CPM effect was observed in healthy dogs: 8.89(0.58) N pre- versus 9.5(0.38) N post-conditioning stimulus ( p = 0.032). Mechanical threshold of laboratory dogs at baseline was high 9.44(1.1) N with no CPM effect observed. Primary tactile allodynia was present in OA-induced dogs ( p = 0.014), and mechanical threshold was decreased in OA-induced 2.72(0.74) N, and OSA dogs 6.69 (2.35) N; ( p < 0.05 for both painful conditions). Secondary mechanical allodynia was noted in OA-induced dogs ( p = 0.013). No CPM effect was observed in OSA or OA-induced dogs. Discussion: Deficiency of descending inhibitory control is a risk factor for the development of chronic pain in people. Similar deficiency was noted in dogs with chronic pain. The lack of CPM effect of laboratory dogs at baseline suggests a situation of stress-induced analgesia. Acknowledgement: This study was partially funded by Le Groupe Vétoquinol. The authors wish to thank the staff at ArthroLab Inc. and Centre Vétérinaire Rive-Sud, as well as the dogs who participated in these studies. Dr. Beatriz Monteiro is a recipient of the Vanier Canada Graduate Scholarship, and this program of research is funded (Pr. Eric Troncy) by the Natural Sciences and Engineering Research Council of Canada as well as the Canada Foundation for Innovation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".