Accuracy of urinary dipstick for glucose and protein determination in canine cerebrospinal fluid
Bibliographic record
Abstract
OBJECTIVE: To evaluate the precision of urinary dipstick (UD) to assess protein and glucose concentrations in canine CSF samples compared to the standard methods. METHODS: Cerebrospinal fluid protein and glucose were measured in 22 samples from dogs with neurological diseases affecting the CNS using UD and biochemistry (pyrogallol red and glucose oxidase reaction, respectively). Results were converted into scores to allow comparison between methods. The proportion of divergence between methods and its CI were calculated. The sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), and accuracy (Ac) of UD were determined for 2 cutoff levels of CSF protein (15 and 30 mg/dL) and glucose (40 and 100 mg/dL). RESULTS: The proportion of divergence between methods was 64% (95% CI, 44% to 84%) for CSF protein (representing 14 of 22 erroneous samples), of which 92.9% (13 of 14) had a UD score lower than biochemistry. For CSF glucose, 73% (16 of 22 erroneous samples; 95% CI, 54% to 91%) had divergence between methods, of which 87.5% (14 of 16) had a UD score higher than biochemistry. Urinary dipstick had better results when the cutoff level was 15 mg/dL for protein (Se, 78.9%; Sp, 66.7%; PPV, 93.7%; NPV, 33.3%; Ac, 77.3%) and 40 mg/dL for glucose (Se, 89.5%; Sp, 33.3%; PPV, 89.5%; NPV, 33.3%; Ac, 81.8%) concentrations. CONCLUSIONS: Urinary dipstick was unreliable in estimating canine CSF protein and glucose concentrations. CLINICAL RELEVANCE: The UD underestimated protein and overestimated glucose levels in the canine CSF, which could have a significant diagnostic impact and should discourage its use as a bedside test.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".