Assessing patient frailty in plastic surgery: A systematic review
Bibliographic record
Abstract
PURPOSE: With the increase in the aging population, the level of frailty has become an important metric for assessing preoperative patient risk. Although medical and surgical specialties continue to adopt and standardize the use of frailty instruments, few studies within the plastic surgery literature have utilized such instruments to predict poor postoperative outcomes. The purpose of this article is two-fold: (1) to provide a comprehensive review of the existing frailty instruments and (2) to summarise the existing evidence regarding the role of pre-operative frailty assessments on peri‑operative morbidity and mortality in plastic surgery. METHODS: This systematic review was registered a priori on the Open Science Framework (https://osf.io/vfzw8). A computerized database search of Ovid MEDLINE, EMBASE, and Cochrane was performed from database inception to December 13, 2020. All articles that examined the effect of preoperative patient frailty on perioperative morbidity and mortality outcomes following plastic surgery interventions were included for data extraction. RESULTS: From the 11 studies included in this review, ten unique frailty instruments were identified. The modified Frailty Index (mFI) and the Fried Frailty Index (FFI) were the most commonly reported frailty measurement tools; however, the FRAIL scale was the only outcome measure identified to be valid, reliable, and responsive to change. Regardless of the frailty measure used, nearly all studies reported that worse surgical outcomes were associated with a higher patient frailty score. CONCLUSION: There is a strong association between higher frailty scores and worse postoperative outcomes in plastic surgery. The FRAIL scale is a clinimetrically sound frailty instrument that should be used in all patients to assess perioperative risk in plastic surgery.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".