Functional decline after major elective non‐cardiac surgery: a multicentre prospective cohort study
Bibliographic record
Abstract
Self-reported postoperative functional recovery is an important patient-centred outcome that is rarely measured or considered in research and decision-making. We conducted a secondary analysis of the measurement of exercise tolerance before surgery (METS) study for associations of peri-operative variables with functional decline after major non-cardiac surgery. Patients who were at least 40 years old, had or were at risk of, coronary artery disease and who were scheduled for non-cardiac surgery were recruited. Primary outcome was a reduction in mobility, self-care or ability to conduct usual activities (EuroQol 5 dimension) from before surgery to 30 days and 1 year after surgery. A decline in at least one function was reported by 523/1309 (40%) participants at 30 days and 320/1309 (24%) participants at 1 year. Participants who reported higher pre-operative Duke Activity Status indices more often reported functional decline 30 days after surgery and less often reported functional decline 1 year after surgery. The odds ratios (95%CI) of functional decline 30 days and 1 year after surgery with moderate or severe postoperative complications were 1.46 (1.02-2.09), p = 0.037 and 1.44 (0.98-2.13), p = 0.066. Discrimination of participants who reported functional decline 30 days and 1 year after surgery were poor (c-statistic 0.61 and 0.63, respectively). In summary, one quarter of participants reported functional decline up to one postoperative year.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".