Frailty assessment tools and their predictive value in preoperative evaluation for general surgery: a narrative review
Bibliographic record
Abstract
The world’s population is ageing rapidly, with projections suggesting a significant increase in the elderly demographic over the next few decades. This shift is likely to result in long periods of infirmity and frailty, thus escalating healthcare demands. Frailty has been investigated almost exclusively in the elderly because of its association with the senior population. It is considered to be an unavoidable aspect of old age, rather than a variable aspect of ageing. It has been linked to a greater risk of falls and decreased mobility, increased hospitalizations, institutionalization, and mortality. Due to elevated complication risk, the frail group required expert surgical treatment and specialised care. Various models have been described in recent years, including the Frailty Phenotype model, the Edmonton Frailty scale, and the Clinical Frailty scale, among many others. Each model employs several components to assess an individual’s frailty. There is, however, no gold standard for measuring fragility. The use of such assessment methods in pre-operative frailty screening as a risk-stratification instrument has been shown to be effective in older persons undergoing general surgery. The use of frailty assessment tools to determine the level of frailty together with evidence-based treatment and management should be supported by collaborative decision making. In this narrative review, we discussed the four commonly used frailty assessment tools with a particular emphasis on the need for additional research to refine these models and enhance their predictive accuracy in surgical settings.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| 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) | 0.002 | 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".