Incidence of late-onset hyperlactatemia and association with clinical outcomes in intensive care patients
Bibliographic record
Abstract
OBJECTIVE: To perform a systematic literature review to summarise current evidence of the incidence and clinical impact of late-onset hyperlactatemia in intensive care patients about case-fatality and morbidity. METHODS: MEDLINE, EMBASE, and ClinicalTrials.gov were searched using medical subject headings from database inception to 27 November 2024. Before the search, the protocol was registered on the International Prospective Register of Systematic Reviews (PROSPERO). Two independent reviewers screened the search results, and studies were included if they were original research that assessed late-onset hyperlactatemia in critically ill patients. Risk of bias was assessed using the Newcastle-Ottawa Scale, and the data were analysed using a descriptive approach without meta-analysis. RESULTS: Of the 10,388 screened studies, 6 were included in the final manuscript, 5 retrospective and 1 prospective. All were assessed as good quality studies. Five were cardiac surgical patients, and one was general intensive care patients. All six studies reported the incidence of late-onset hyperlactatemia, which ranged from 8.5 to 70.8%. Two studies reported increased intensive care unit and/or hospital case-fatality with late-onset hyperlactatemia; however, small absolute numbers limited the interpretability. CONCLUSION: The limited data regarding late-onset hyperlactatemia make it difficult to draw significant conclusions regarding the relationship to clinical outcomes. However, the few available studies suggest that it is a common finding and highlight the need for further research to assess the underlying aetiologies and association with clinical outcomes.
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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 | 0.012 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".