Nutrigenomics, β-Cell Function and Type 2 Diabetes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it