Prognostic value of anion gap for patients with heart failure: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Heart failure (HF) is among the cardiovascular diseases with high morbidity and mortality worldwide. Due to the high burden of HF, finding easy-to-use prognostic factors has become important. Studies have investigated the correlation between anion gap (AG) and the HF prognosis. In this systematic review and meta-analysis, we aimed to evaluate the association between AG association with HF prognosis. METHODS: PubMed, Embase, Scopus, and the Web of Science were systematically searched for studies evaluating AG in HF prognosis. Standardized mean difference (SMD) and pooled hazard ratio (HR) in addition to 95% confidence intervals (CIs) were calculated using random-effect meta-analyses to compare survivors vs. non-survivors. RESULTS: = 46.4%). Meta-analysis of HRs for assessment of mortality revealed that high AG levels had significantly higher hazards of mortality, compared with low AG group (HR 1.64, 95% CI 1.35 to 1.99, P < 0.0001). Finally, a study investigated the association between intensive care unit (ICU) length of stay and AG in patients with HF which showed no significant association. CONCLUSION: This study found that higher AG levels are associated with higher mortality in patients with HF which could be used in clinical settings and for patient management due to its ease of measurement and calculation. If confirmed in future studies, using this easy-to-measure index in clinical settings could provide useful information for clinicians in determining the risk of HF patients. CLINICAL TRIAL NUMBER: Not applicable.
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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.035 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".