Evaluation of the ADVIA 120 for analysis of canine cerebrospinal fluid
Bibliographic record
Abstract
BACKGROUND: Conventional techniques for canine cerebrospinal fluid (CSF) analysis require large sample volumes and are labor intensive and subject to operator variability. OBJECTIVE: The purpose of this study was to evaluate the ADVIA120 CSF assay for analysis of canine CSF samples. METHODS: CSF samples collected from 36 healthy control dogs and 17 dogs with neurologic disease were processed in parallel using the automated assay and established manual methods using a hemocytometer and cytocentrifugation. Results for WBC (total nucleated cell) count, RBC count, and differential nucleated cell percentages were compared using Spearman rank correlation coefficients and Bland-Altman bias plots. RESULTS: Correlation coefficients for WBC and RBC counts were 0.57 and 0.83 for controls, and 0.92 and 0.94 for ill dogs, respectively. Coefficients for the percentages of neutrophils, lymphocytes, and monocytes were 0.53, 0.26, and 0.12 for controls and 0.77, 0.92, and 0.70 for dogs with neurologic disease. When data were combined (n=53), correlation coefficients were 0.86 and 0.91 for WBC and RBC counts, and 0.63, 0.43, and 0.30 for neutrophil, lymphocyte, and monocyte percentages. A 9.5% positive bias and 7.0% negative bias were obtained for the ADVIA 120 CSF assay for lymphocytes and macrophages in dogs with neurologic disease with Bland-Altman analysis. A 12.2% positive bias was found for lymphocyte percentage in dogs with neurologic disease. CONCLUSIONS: Manual and automated CSF assays had moderate to excellent correlation for WBC and RBC concentrations, but results were more variable for differential cell percentages. The ADVIA assay may be more useful for assessment of canine CSF with adjustment of cell differentiation algorithms.
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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 | 0.007 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".