Longitudinal Assessment of Frailty and Quality of Life in Patients Undergoing Head and Neck Surgery
Bibliographic record
Abstract
OBJECTIVE: To understand changes in frailty and quality of life (QOL) in frail versus non-frail patients undergoing surgery for head and neck cancer (HNC). METHODS: Prospective cohort study of patients (median age 67 (50, 88)) with HNC undergoing surgery from December 2011 to April 2014. Fried's Frailty Index, Vulnerable Elders Survey (VES-13), and comprehensive QOL assessments (EORTC QLQ-C30 and HN35) were completed at baseline and 3, 6, and 12-month post-operative visits. Change in frailty and QOL over time was compared between frailty groups (non-frail (score 0), pre-frail (score 1-2), and frail (score 3-5)) using a mixed effects model. Predictors of long-term elevated frailty (12 months > baseline) were analyzed using logistic regression. RESULTS: The study had 108 patients classified as non-frail (47%), 104 pre-frail (mean (SD) 1.3 (0.4), 45%), and 17 frail (3.4 (0.6); 7%). Frailty score decreased significantly for frail patients 3 months post-operatively (2.1 (1.0); P = .002) and remained significantly lower than baseline at 6 and 12 months (2.1 (1.4); P = .0008 and 2.2 (1.5); P = .005, respectively) while frailty score increased for non-frail patients at 3 months (1.1 (1.0); P < .001) and then decreased. Forty-eight patients (21%) had long-term elevated frailty, with baseline frailty and marital status identified as predictors on univariate analysis. The frail population had significantly worse QOL scores at baseline, which persisted 12 months post-operatively. CONCLUSIONS: Frail patients demonstrate a decrease in frailty score following surgical treatment of HNC. Frail patients have significantly worse QOL scores on longitudinal assessment and would benefit from supportive services throughout their care. LEVEL OF EVIDENCE: 3 Laryngoscope, 131:E2232-E2242, 2021.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".