Cerebrospinal Fluid Analysis in Recumbent Adult Dairy Cows With or Without Spinal Cord Lesions
Bibliographic record
Abstract
BACKGROUND: Diagnosis of central nervous system (CNS) lesions in recumbent dairy cattle (RDC) is challenging because neurologic examination is limited and medical imaging often is challenging or unrewarding. Cerebrospinal fluid (CSF) analysis is useful in the diagnosis of CNS disorders in cattle. However, its utility in identifying spinal cord lesions in RDC remains to be evaluated. HYPOTHESIS/OBJECTIVES: We hypothesized that CSF analysis would discriminate between RDC with and without spinal cord lesions. ANIMALS: Twenty-one RDC with spinal cord lesions (RDC+) and 19 without (RDC-) were evaluated. METHODS: Spinal cord lesions were confirmed at necropsy. Signalment, clinical findings, and CSF results were compared retrospectively. Total nucleated cell count and differential, protein concentration, and red blood cell count in RDC+ and RDC- were compared. RESULTS: Neoplasia, trauma, and infectious processes were the most frequent spinal cord lesions identified. Cerebrospinal fluid protein concentrations and TNCC were significantly higher in RDC+ compared to RDC- (P = .0092 and P = .0103, respectively). Additionally, CSF protein concentrations and TNCC in RDC- were lower than previously published reference ranges. Using an interpretation rule based on CSF protein concentration and TNCC, it was possible to accurately identify 13 RDC with spinal cord lesions and 6 RDC without lesions. It was not possible to determine spinal cord status in the remaining 18 RDC. CONCLUSIONS AND CLINICAL IMPORTANCE: Cerebrospinal fluid analysis is valuable in the evaluation of spinal cord status in RDC. The prognosis associated with these findings remains to be determined.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".