Impact of long-term storage on mid-infrared spectral patterns of serum and synovial fluid samples of dogs with osteoarthritis
Bibliographic record
Abstract
Abstract Objective To evaluate impact of long-term storage on mid-infrared (MIR) spectral patterns of serum and synovial fluid (SF) of dogs with knee OA and controls. Design Serum (52 OA and 49 control) and SF (51 OA and 51 control) samples from dogs that had been in short-term (<3 years) frozen state (−80°C) had their MIR spectra obtained. The remaining aliquots were maintained in long-term (>5 years) frozen state before having MIR spectra acquired under the same testing conditions. Multi-level simultaneous component analysis was used to evaluate the effect of time. Partial least squares discriminant analysis was used to compare performance of predictive models built for discriminating OA from control spectra from each time point. Results Median interval of storage between sample measurements was 5.7 years. Spectra obtained at two time points were significantly different ( P <0.0001), however, contribution of sample aging accounted for only 1.61% and 2.98% of 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, values of the same figures of merit were 92.5±2.6%, 97.1±1.7% and 94.8±1.4%, respectively. Predictive models based on short-term stored SF spectra had 97.3±1.6% sensitivity, 89.4±2.6% specificity and 93.4±1.6% accuracy, while the values for long-term storage 95.7±2.1%, 95.7±0.8% and 95.8±1.1%, respectively. Conclusions Long-term storage of serum and SF results in significant differences in spectral variables, however, these changes do not significantly alter the performance of predictive algorithms for discriminating OA samples 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".