Clinical tools to assess functional capacity before elective non-cardiac surgery: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this scoping review is to map the evidence on clinical tools to assess functional capacity prior to elective non-cardiac surgery. INTRODUCTION: Functional capacity is a strong prognostic indicator before surgery, which can be used to identify patients at elevated risk of postoperative complications, yet, there is no consensus on which clinical tools should be used to assess functional capacity in patients prior to non-cardiac surgery. INCLUSION CRITERIA: This review will consider any randomized or non-randomized studies that evaluate the performance of a functional capacity assessment tool in adults (≥18 years) prior to non-cardiac surgery. For studies to be included, the tool must be used clinically for risk stratification. We will exclude studies on lung and liver transplant surgery, as well as ambulatory procedures performed under local anesthesia. METHODS: The review will be conducted in line with the JBI methodology for scoping reviews. A peer-reviewed search strategy will be used to query relevant databases (ie, MEDLINE, Embase, EBM Reviews). Additional sources of evidence will include databases of non-peer-reviewed literature and the reference lists of included studies. Two independent reviewers will identify eligible studies in 2 stages: stage 1, based on titles and abstracts; and stage 2, based on full texts. Information on study details, measurement properties, pragmatic qualities, and/or clinical utility metrics will be charted in duplicate onto standardized data collection forms. The results will be presented using descriptive summaries, frequency tables, and visual plots that highlight the extent of evidence and remaining gaps in the validation process of each tool. REVIEW REGISTRATION: Open Science Framework https://osf.io/6nfht.
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 | 0.119 | 0.090 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.011 | 0.014 |
| Bibliometrics | 0.021 | 0.014 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.065 | 0.016 |
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".