Identification of infrared absorption spectral characteristics of synovial fluid of horses with osteochondrosis of the tarsocrural joint
Bibliographic record
Abstract
OBJECTIVE: To determine the feasibility of the use of Fourier-transform infrared (FTIR) spectroscopy within the midinfrared range to differentiate synovial fluid samples of joints with osteochondrosis from those of control samples. ANIMALS: 33 horses with osteochondrosis of the tarsocrural joint and 31 horses free of tarsocrural joint disease. PROCEDURES: FTIR spectroscopy of synovial fluid was used. Sixty-four synovial fluid samples from the tarsocrural joint were collected. Of these, 33 samples were from horses with radiographic evidence of osteochondrosis of the tarsocrural joint and 31 from control joints. Disease-associated features within infrared spectra of synovial fluid were statistically selected for spectral classification, and the variables identified were used in a classification model. Linear discriminant analysis and leave-one-out cross-validation were used to develop a classifier to identify joints with osteochondrosis. RESULTS: 12 significant subregions were identified that met the selection criteria. The stepwise discriminant procedure resulted in the final selection of 6 optimal regions that most contributed to the discriminatory power of the classification algorithm. Infrared spectra derived from synovial fluid of joints with osteochondrosis were differentiated from the control samples with accuracy of 77% (81% specificity and 73% sensitivity). CONCLUSIONS AND CLINICAL RELEVANCE: The disease-associated characteristics of infrared spectra of synovial fluid from joints with osteochondrosis may be exploited via appropriate feature selection and classification algorithms to differentiate joints with osteochondrosis from those of control joints. Further study with larger sample size including age-, breed-, and sex-matched control horses would further validate the clinical value of infrared spectroscopy for the diagnosis of osteochondrosis in horses.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".