Blood concentrations of<scp>d</scp>‐ and<scp>l</scp>‐lactate in healthy rabbits
Bibliographic record
Abstract
OBJECTIVES: To determine whole blood and serum concentrations of l-lactate and serum concentrations of d-lactate in healthy rabbits and compare three methods of analysis for l-lactate measurement. METHODS: Prospective study using 25 rabbits. Concentrations of whole blood l-lactate were measured using a portable analyser and a blood gas analyser. The remainder of the sample was allowed to clot for centrifugation. Serum was stored at -20°C for determination of l- and d- lactate by high-performance liquid chromatography. RESULTS: d-lactate values by high-performance liquid chromatography were 0 · 17 ± 0 · 08 mmol/L. l-lactate values were 5 · 1 (±2 · 1) mmol/L by high-performance liquid chromatography, 6 · 9 (±2 · 7) mmol/L with the portable analyser and 7 · 1 (±1 · 6) mmol/L with the blood gas analyser. No significant difference (P > 0 · 05) was found between the two analysers. Significant difference existed between serum l-lactate values obtained by high-performance liquid chromatography and the whole blood values obtained with the blood gas analyser (P < 0 · 01) and portable analyser (P < 0 · 05). CLINICAL SIGNIFICANCE: Serum concentrations of d-lactate in healthy rabbits are in the range of those of other mammals. l-lactate values in healthy rabbits are higher compared with other mammals. Good correlation was found between the portable and blood gas analysers for whole blood l-lactate measurement in healthy rabbits.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 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".