Infrared spectroscopy of synovial fluid as a potential screening approach for the diagnosis of naturally occurring canine osteoarthritis associated with cranial cruciate ligament rupture
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".