Effect of time until sample analysis on lactate in dogs with shock
Bibliographic record
Abstract
INTRODUCTION: Lactate concentrations can increase with hypoperfusion in dogs and could be used as a prognostic indicator in sick dogs. In a busy emergency service, sample evaluation could be delayed. However, sample evaluation delays have been shown to cause lactate concentration increases in healthy dogs. In sick dogs, the magnitude of increased lactate is unknown. The goal of this study was to prospectively evaluate the effect of room temperature storage times on lactate measurements in dogs presenting to an emergency service. METHODS: We evaluated the precision and accuracy of the NOVA Lactate Plus, using standard procedures. To assess the impact of time on lactate concentrations in sick dogs, we prospectively enrolled dogs presenting to an emergency service. Lactate concentrations were measured at six time points using samples stored at room temperature. A Friedman test, followed by a Wilcoxon rank test with a Bonferroni correction was used to evaluate time points. RESULTS: of .98, and a mean bias of 0.26 in 50 canine samples. Precision was acceptable, with a percent coefficient of variation of 5.39. Statistically significant increases in lactate concentrations were found at all time points over baseline (P = .008). CONCLUSIONS: In as little as 7.5 minutes, lactate concentrations increased significantly in samples stored at room temperature. Dogs with lower initial lactate concentrations had had higher increases in lactate concentration percentages over 90 minutes.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".