Comparison of glucose concentrations in blood samples obtained with a marginal ear vein nick technique versus from a peripheral vein in healthy cats and cats with diabetes mellitus
Bibliographic record
Abstract
OBJECTIVE: To compare blood glucose (BG) concentrations measured with a portable blood glucose meter in blood samples obtained with a marginal ear vein (MEV) nick technique, from a peripheral venous catheter, and by direct venipuncture in healthy cats and cats with diabetes mellitus. DESIGN: Prospective study. ANIMALS: 1 0 healthy cats and 11 cats with diabetes mellitus. Procedure-On day 1, blood samples were collected every hour for 10 hours by the MEV nick technique and from a peripheral venous catheter. On day 2, blood samples were collected every hour for 10 hours by the MEV nick technique and by direct venipuncture of the medial saphenous vein. RESULTS: For all cats, mean BG concentration for samples collected by the MEV nick technique was not significantly different from mean concentration for samples obtained from the peripheral venous catheter. For healthy cats, mean BG concentration for samples collected by the MEV nick technique was not significantly different from mean concentration for samples obtained by direct venipuncture. For cats with diabetes mellitus, mean BG concentration for samples collected by the MEV nick technique was significantly different from mean concentration for samples obtained by direct venipuncture; however, for the range of concentrations examined, this difference was not clinically important. CONCLUSIONS AND CLINICAL RELEVANCE: Results suggest that for the range of concentrations examined, the MEV nick technique is a reasonable alternative to venous blood collection for serial measurement of BG concentrations in cats.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".