PSV-B-28 Late-Breaking: Development and validation of a Total Inflammation Index™ for identifying inflammation in Labrador Retrievers using a pressure walkway
Bibliographic record
Abstract
Abstract The objective of this trial was to develop an index system to identify inflammation in Labrador Retrievers using a pressure walkway system. Gait analysis data can be difficult to interpret between treatment groups or for identifying low grade inflammation. To calculate the Total Inflammation Index™, the distance away from the ideal score was calculated for four parameters for each dog, including gait lameness score, total pressure index, step/stride ratio, and hind reach. These values were equally weighted and added together to produce the Total Inflammation Index™. For validation, the Total Inflammation Index™ values were compared to biomarker data for inflammation including cartilage oligomeric matrix protein, interleukin-6, creatine kinase, and c-reactive protein. Forty Labrador Retrievers (20 male/20 female) were used in this trial. All dogs were passed over the pressure walkway (Gait4Dogs; CIR Systems, Inc) to obtain gait analysis at baseline, 24h prior to the first 5km run, 24h after the first 5km run, 24h prior to the final 16km run, and 24h after the final 16km run. All biomarkers and the Total Inflammation Index™ were both significantly lower at the pre-exercise timepoints and elevated after post-exercise timepoints (P < 0.01). The Total Inflammation Index™ had significant correlation between timepoints and all biomarkers, including cartilage oligomeric matrix protein (P < 0.01), interleukin-6 (P < 0.05), creatine kinase (P < 0.01), and c-reactive protein (P < 0.05). The Total Inflammation Index™ appears to be a valid assay to evaluate generalized inflammation in Labrador Retrievers, and is in agreement with inflammatory biomarker values.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".