Ex vivo changes in blood glucose levels seldom change blood glucose control algorithm recommendations.
Bibliographic record
Abstract
BACKGROUND: Hyperglycemia and glycemic variabilities are associated with adverse outcomes in critically ill patients. Blood glucose control with insulin mandates an adequate and precise assessment of blood glucose levels. Blood glucose levels, however, can change ex vivo after sampling. The aim of this study was to determine whether this phenomenon affects the practice of blood glucose control. METHODS: We performed an observational study in a mixed medical-surgical intensive care unit (ICU). ICU nurses were the primary healthcare workers involved in the practice of blood glucose control, and they used an insulin-titration method and blood-sampling algorithm aimed at maintaining blood glucose levels between 5 to 8 mmol/L. RESULTS: Blood glucose levels were measured directly after sampling, as well as after 30 and 60 minutes using the same samples. Blood glucose control algorithm recommendations were scored for each measurement. We collected 450 blood samples from 74 patients (median of 3 [2-8] samples per patient). The mean ex vivo changes in the blood glucose level were rather small (-0.1±1.6 mmol/L (range -1.4 to 0.7) and -0.2±1.6 mmol/L (range -1.3 to 0.5) at 30 and 60 minutes after sampling, respectively; P<0.05). An ex-vivo change in the blood glucose level hardly ever resulted in a change in algorithm recommendation (4% and 6% at 30 and 60 minutes after sampling, respectively). In most cases the algorithm advised a lower insulin infusion speed. CONCLUSION: Ex vivo changes in blood glucose levels, although statistically significant, seem clinically irrelevant.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".