Lactate clearance as a useful biomarker for the prediction of all-cause mortality in critically ill patients: a systematic review study protocol
Bibliographic record
Abstract
INTRODUCTION: Arterial lactate is a reflection of balance between lactate production and clearance. Accumulating lactate may be a marker of global hypoxia or hypoperfusion. Lactate clearance is the reduction of lactate concentrations with interventional strategies, and it has been associated with increased risk of death in critically ill patients. However, conflicting results exist, which mandates a systematic review to clarify the association between lactate clearance and clinical outcome. METHODS AND ANALYSIS: Critically ill adult patients will be included in our analysis. This population will include heterogeneous study participants, including patients with sepsis or severe sepsis, trauma, surgical intensive care unit (ICU) patients, and so on. We will search four databases including EBSCO, PubMed, Scopus and ISI Web of knowledge from inception to February 2014. There will be no language restrictions in the electronic search for studies. Newcastle Ottawa Scale for cohort study will be employed to assess the reporting quality of included original studies. We will report pooled relative risk of death for those with lactate clearance and those without. The diagnostic performance of lactate clearance in predicting mortality will be explored by using the hierarchical summary receiver operating characteristic model. ETHICS AND DISSEMINATION: The protocol for the systematic review has been registered in PROSPERO. The study will be disseminated electronically and in print. It will also be presented to conferences related to critical care medicine. TRIAL REGISTRATION NUMBER: CRD42013006511.
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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 | 0.052 | 0.068 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.018 | 0.013 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.052 | 0.006 |
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".