The effect of blood contamination on equine synovial fluid analysis
Bibliographic record
Abstract
OBJECTIVE: Based on a systemic complete blood count and a synovial fluid sample, to develop a mathematical model enabling the approximation of corrected values for synovial fluid white blood cell (WBC) count, neutrophil percentage, and total protein concentration in samples of synovial fluid that were contaminated by blood. METHODS: Peripheral venous blood and synovial fluid samples were obtained from ten horses. A pooled synovial fluid sample from each horse was separated into 2 mL aliquots, which were subsequently contaminated with a known percentage of autogenous blood (0 to 50% of the synovial fluid volume). A complete blood count, packed cell volume, total protein (TP) concentration, and differential cytological examination were performed in all the synovial fluid and venous blood samples. Regression analysis was used to generate a model to calculate non-contaminated synovial WBC count, TP concentration and synovial neutrophil percentage. Using a further five horses these models were applied in blinded fashion to contaminated synovial fluid samples. Calculated values were compared to non-contaminated measured values. RESULTS: Model results for synovial WBC count and TP concentration were not significantly different from measured values. Calculated neutrophil percentage of synovial fluid WBC was a mean of 6.6% higher than measured values and was significantly different. There was no effect of the severity of contamination (as a percentage of volume) on the ability of the models to predict any of the outcome variables. CLINICAL SIGNIFICANCE: It is possible to calculate non-contaminated synovial fluid WBC and TP values, but not neutrophil percentage, from heavily contaminated samples. Further study would allow for improved prediction, validation and extrapolation to a wider horse population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".