Frailty and risk of complications in head and neck oncologic surgery. Systematic review and dose-response meta-analysis
Bibliographic record
Abstract
BACKGROUND: There is emerging evidence that frail individuals present a decreased physiological reserve, decreased ability to maintain homeostasis, and increased vulnerability to stressors. The concept of frailty has become increasingly recognized as a valuable measure in oncological surgical patients, including those with head and neck cancer. Preoperative screening for frailty may provide an individualized risk assessment that can be used by an interdisciplinary team for preoperative counseling and to improve outcomes. The aim of this meta-analysis was to evaluate the relationship between frailty and the risk of major postoperative complications in frail individuals submitted to head and neck oncologic surgery. MATERIAL AND METHODS: PubMed, SCOPUS, Web of Science, Google Scholar and OpenThesis were systematically searched to identify studies that evaluated the risk of major postoperative complications in frail individuals undergoing head and neck oncologic surgery. The search was performed on August 31, 2020, without language or date restrictions. Two independent investigators screened the searched studies based on each paper's title and abstract. Relevant studies were read in full and selected according to the eligibility criteria. Frailty was assessed by modified Frailty Index (mFI-11) and major postoperative complications were measured by the Clavien-Dindo classification. We performed a categorical and dose-response meta-analysis using a random-effects model to evaluate the association between frailty and the risk of major postoperative complications in patients submitted to head and neck oncologic surgery. The results of the meta-analysis were expressed as relative risk (RR) and 95% confidence interval (95% CI). The risk of bias was assessed using the Newcastle-Ottawa Scale (NOS). RESULTS: Four studies (9,947 patients) were included in this systematic review and meta-analysis. Frail patients presented an increased risk of life-threatening complications requiring intensive care unit (ICU) admission (RR = 4.67; 95% CI 1.54-14.10) and 30-day mortality (RR = 8.10; 95% CI 2.30-28.57) compared to non-frail patients. We found evidence of dose-response trend between mFI-11 and major postoperative complications. CONCLUSIONS: Higher frailty scores are associated with a significant increase in ICU-level complications and 30-day mortality after head and neck oncologic 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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.050 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".