Gender Differences in Survival after Coronary Artery Bypass Grafting—13-Year Results from KROK Registry
Bibliographic record
Abstract
The influence of gender on both early and long-term outcomes of coronary artery bypass grafting (CABG) is not clearly defined. Objectives: This study aimed to assess the impact of gender on early and long-term mortality after CABG using data from the KROK Registry. Methods: All 133,973 adult patients who underwent CABG in Poland between 1 January 2009 and 31 December 2019 were included in the Polish National Registry of Cardiac Surgical Procedures (KROK Registry). The study enrolled 90,541 patients: 68,401 men (75.55%) and 22,140 women (24.45%) who met the inclusion criteria. Then, 30-day mortality, 1-year mortality, and long-term mortality rates were compared. Results: Advanced age, higher Canadian Cardiovascular Society (CCS) and New York Heart Association (NYHA) grade, diabetes, hypercholesterolemia, arterial hypertension, body mass index BMI > 35 kg/m2, and renal failure, before the propensity matching, were more frequently observed in women. Women more frequently underwent urgent surgery, including single and double graft surgery, and off-pump CABG (OPCAB) (p < 0.001). In propensity-matched groups, early mortality (30 days) was significantly higher in women (3.4% versus 2.8%, p < 0.001). The annual mortality remained higher in this group (6.6% versus 6.0%, p = 0.025). However, long-term mortality differed significantly between the groups and was higher in the male group (33.0% men versus 28.8% women, p < 0.001). Conclusions: There are no apparent differences in long-term mortality between the two sexes in the entire population. In propensity-matched patients, early mortality was lower for men, but the long-term survival was found to be better in women.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".