Clinical Tools to Assess Functional Capacity During Risk Assessment Before Elective Noncardiac Surgery
Bibliographic record
Abstract
BACKGROUND: Functional capacity is critical to preoperative risk assessment, yet guidance on its measurement in clinical practice remains lacking. PURPOSE: To identify functional capacity assessment tools studied before surgery and characterize the extent of evidence regarding performance, including in populations where assessment is confounded by noncardiopulmonary reasons. DATA SOURCES: MEDLINE, EMBASE, and EBM Reviews (until July 2024). STUDY SELECTION: Studies evaluating performance of functional capacity assessment tools administered before elective noncardiac surgery to stratify risk for postoperative outcomes. DATA EXTRACTION: Study details, measurement properties, pragmatic qualities, and/or clinical utility metrics. DATA SYNTHESIS: 6 categories of performance-based tests and 5 approaches using patient-reported exercise tolerance were identified. Cardiopulmonary exercise testing (CPET) was the most studied tool (132 studies, 32 662 patients) followed by field walking tests (58 studies, 9393 patients) among performance-based tests. Among patient-reported assessments, the Duke Activity Status Index (14 studies, 3303 patients) and unstructured assessments (19 studies, 28 520 patients) were most researched. Most evidence focused on predictive validity (92% of studies), specifically accuracy in predicting cardiorespiratory complications. Several tools lacked evidence on reliability (test consistency across similar measurements), pragmatic qualities (feasibility of implementation), or concurrent criterion validity (correlation to gold standard). Only CPET had evidence on clinical utility (whether administration improved postoperative outcomes). Older adults (≥65 years) were well represented across studies, whereas there were minimal data in patients with obesity, lower-limb arthritis, and disability. LIMITATION: Synthesis focused on reported data without requesting missing information. CONCLUSION: Though several tools for preoperative functional capacity assessment have been studied, research has overwhelmingly focused on CPET and only 1 aspect of validity (predictive validity). Important evidence gaps remain among vulnerable populations with obesity, arthritis, and physical disability. PRIMARY FUNDING SOURCE: None. (Open Science Framework: https://osf.io/ah7u5).
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 | 0.018 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.011 |
| 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) | 0.005 | 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".