Nutrigenomics, β-Cell Function and Type 2 Diabetes
Bibliographic record
Abstract
INTRODUCTION: The present investigation was designed to investigate the accuracy and precision of lactate measurement obtained with contemporary biosensors (Chiron Diagnostics, Nova Biomedical) and standard enzymatic photometric procedures (Sigma Diagnostics, Abbott Laboratories, Analyticon). MATERIALS AND METHODS: Measurements were performed in vitro before and after the stepwise addition of 1 molar sodium lactate solution to samples of fresh frozen plasma to systematically achieve lactate concentrations of up to 20 mmol/l. RESULTS: Precision of the methods investigated varied between 1% and 7%, accuracy ranged between 2% and -33% with the variability being lowest in the Sigma photometric procedure (6%) and more than 13% in both biosensor methods. CONCLUSION: Biosensors for lactate measurement provide adequate accuracy in mean with the limitation of highly variable results. A true lactate value of 6 mmol/l was found to be presented between 4.4 and 7.6 mmol/l or even with higher difference. Biosensors and standard enzymatic photometric procedures are only limited comparable because the differences between paired determinations presented to be several mmol. The advantage of biosensors is the complete lack of preanalytical sample preparation which appeared to be the major limitation of standard photometry methods.
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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 | 0.000 |
| 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) | 0.006 | 0.001 |
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".