Anemia as a potent marker of in-hospital mortality in patients admitted to the cardiac intensive care unit: Data from the Critical Care Cardiology Trials Network (CCCTN) Registry
Bibliographic record
Abstract
Background: Anemia is common in critically ill patients and is associated with poor outcomes. We investigated the prevalence of anemia and its association with in-hospital outcomes among patients admitted to cardiac intensive care units (CICUs) and subgroups within this population. Methods: The Critical Care Cardiology Trials Network (CCCTN) is a multicenter network of tertiary CICUs in North America. CICU admissions with available baseline hemoglobin (Hgb) between 2017 and 2023 were included in this analysis. Patients were stratified by Hgb levels (Hgb <8 g/dL, 8 g/dL ≤ Hgb <10 g/dL, 10 g/dL ≤ Hgb <12 g/dL, 12 g/dL ≤ Hgb <14 g/dL, and ≥14 g/dL). The ≥14 g/dL group was used for reference. The association of Hgb level and in-hospital mortality was examined by multivariable logistic regression. Results: =7194). Conclusions: Anemia was present in nearly half of patients admitted to CICUs. Lower admission Hgb is independently associated with higher in-hospital mortality in a graded relationship among patients with cardiac critical illness.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".