Perioperative risk assessment – focus on functional capacity
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review examines how functional capacity informs preoperative risk stratification, as well as strengths and limitations of options for estimating functional capacity. RECENT FINDINGS: Functional capacity (or cardiopulmonary fitness) overlaps with other important characteristics, including muscular strength, balance, and frailty. Poor functional capacity is associated with postoperative morbidity, especially noncardiovascular complications. Both patient interviews and exercise tests are used to assess functional capacity. The usual approach of an unstructured patient interview does not predict outcomes. Structured interviews that incorporate validated questionnaires (Duke Activity Status Index) or standardized questions about physical activity (ability to climb stairs) do predict moderate-or-severe complications and cardiovascular complications. Among exercise tests, cardiopulmonary exercise testing (CPET) has shown the most consistent association with risks of complications. Other tests (6-min walk test, incremental shuttle walk test, stair climbing) might predict complications, but still require further high-quality evaluation. SUMMARY: A straightforward way to better assess functional capacity is a structured interview with validated questionnaires or standardized questions about physical activities. Functional capacity can also be assessed by exercise tests, with the strongest evidence supporting CPET. Although some simpler exercise tests have shown promise, more research remains needed to better define their role in preoperative evaluation.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".