Association of Lactate Levels and Mortality in Intensive Care Units
Bibliographic record
Abstract
Most critically ill patients experience hemodynamic disorders that cause a decrease in blood perfusion, resulting in hypoxia that can increase lactate production. This study will examine the relationship between lactate levels and mortality in intensive care. This study uses a quantitative observational analytical method with a cross-sectional study design and using medical record data. Data analysis was carried out using an Independent T-test to see if there was a significant difference between lactate levels in the blood of dead and living subjects. This study, 154 subjects were obtained, consisting of 96 males (62.3%) and 58 females (37.7%). The average lactate level in the death group was 3.99, and the live group was 2.4 (p=0.004). Subjects with higher lactate levels have higher mortality rates. Lactate levels are one of the indicators of tissue oxygenation in critically ill patients. Hyperlactatemia is common in critically ill patients and can reflect an imbalance between local or systemic oxygen delivery and demand. Hyperlactatemia can cause lactic acidosis or metabolic acidosis and is associated with high mortality outcomes. Increased lactate levels are also associated with the development of Multiple Organ Dysfunction Syndrome (MODS). Subjects with high lactate levels are often associated with a variety of etiologies and poor prognosis, especially critical patients in the Intensive Care Unit (ICU). There was a relationship between blood lactate levels and mortality in ICU patients.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".