Impact of Long-Term Storage on Mid-Infrared Spectral Patterns of Serum and Synovial Fluid of Dogs with Osteoarthritis
Bibliographic record
Abstract
Mid-infrared spectral (MIR) patterns of serum and synovial fluid (SF) are candidate biomarkers of osteoarthritis (OA). The impact of long-term storage on MIR spectral patterns was previously unknown. MIR spectra of canine serum (52 knee-OA, 49 control) and SF (51 knee-OA, 51 control) were obtained after short-term and long-term storage in −80 °C. Multilevel simultaneous component analysis and partial least squares discriminant analysis were used to evaluate the effect of time and compare the performance of predictive models for discriminating OA from controls. The median interval of storage between sample measurements was 5.7 years. Spectra obtained at two time points were significantly different (p < 0.0001); however, sample aging accounted for only 1.61% and 2.98% of the serum and SF profiles’ variability, respectively. Predictive models for discriminating serum of OA from controls for short-term storage showed 87.3 ± 3.7% sensitivity, 88.9 ± 2.4% specificity, and 88.1 ± 2.3% accuracy, while for long-term storage, they were 92.5 ± 2.6%, 97.1 ± 1.7%, and 94.8 ± 1.4%, respectively. Predictive models of short-term stored SF spectra had 97.3 ± 1.6% sensitivity, 89.4 ± 2.6% specificity, and 93.4 ± 1.6% accuracy, while for long-term storage they were 95.7 ± 2.1%, 95.7 ± 0.8%, and 95.8 ± 1.1%, respectively. Long-term storage of serum and SF resulted in significant differences in MIR spectral variables without significantly altering the performance of predictive algorithms for discriminating OA from controls.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".