Infrared spectroscopy of synovial fluid as a potential screening approach for the diagnosis of naturally occurring canine osteoarthritis associated with cranial cruciate ligament rupture
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Bibliographic record
Abstract
Objective: To evaluate infrared (IR) spectroscopy of synovial fluid (SF) as tool to differentiate between knees of dogs with naturally occurring OA associated with cranial cruciate ligament rupture (CrCLR) and controls. Method: 104 adult dogs with CrCLR (affected group) and 50 adult control dogs were recruited in a prospective observational study. Synovial fluid (SF) samples were collected preoperatively from dogs with CrCLR and from a subset of these at 4-, and 12-week post-surgery. Knee samples were collected bilaterally once from control dogs. Dried synovial fluid films were made, and IR absorbance spectra acquired. After preprocessing, partial least squares discriminant analysis (PLS-DA) and ANOVA-simultaneous component analysis (ASCA) were used to evaluate group and temporal differences, and to develop predictive models. Results: There were statistically significant spectral differences between the SF of OA affected and control dogs at all three time-points (P < 0.001). Pairwise comparison of spectral SF of knees with CrCLR over time showed statistically significant differences amongst all three time-points (P < 0.001). The predictive model for identifying the affected group from control had sensitivity, specificity and overall accuracy of 97.6%, 99.7% and 98.6%, respectively. Conclusions: The findings demonstrate the ability of FTIR-spectroscopy of synovial fluid combined with chemometric methods to accurately differentiate dogs with OA secondary to CrCLR from controls. The role of this IR-based screening test as a diagnostic and monitoring biomarker for OA specific to the joint being sampled warrants further investigation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it