Gait Speed and 1‐Year Mortality Following Cardiac Surgery: A Landmark Analysis From the Society of Thoracic Surgeons Adult Cardiac Surgery Database
Bibliographic record
Abstract
Background In older adults undergoing cardiac surgery, prediction of downstream risk is critical. Our objective was to determine the association of 5-m gait speed with 1-year mortality and repeat hospitalization following cardiac surgery. Methods and Results In this prospective cohort of patients undergoing cardiac surgery at centers participating in the Society of Thoracic Surgeons Database with gait speed recorded, we examined all-cause mortality using a landmark analysis at 0 to 30, 30 to 365, and >365 days, as well as repeat hospitalization. The cohort consisted of 8287 patients (median age, 74 years; 32% females). At 1 year, survival was 90% in the slow (<0.83 m/s), 95% in the middle (0.83-1.00 m/s), and 97% in the fast (>1.00 m/s) gait speed tertiles, and risk of hospitalization was 45%, 33%, and 27%, respectively (both P<0.0001). After adjustment, gait speed remained predictive of mortality (hazard ratio, 2.16 per 0.1-m/s decrease in gait speed; 95% confidence interval, 1.59-2.93) and rehospitalization (hazard ratio, 1.71 per 0.1-m/s decrease in gait speed; 95% confidence interval, 1.45-2.0). In a landmark analysis, the effect of slow gait speed on mortality was most marked from 30 to 365 days after surgery, where each decline in 0.1 m/s of gait speed conferred a 2-fold increased risk of mortality. Conclusions Gait speed is a simple tool to screen for frailty and identify older adults at risk for adverse events in the early and midterm postoperative periods.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".