Frailty as a predictor of outcomes in patients undergoing head and neck cancer surgery
Bibliographic record
Abstract
OBJECTIVES: To evaluate whether frailty and functional measures are predictors of perioperative complications and length of hospital stay (LOS) in patients undergoing head and neck cancer surgery. STUDY DESIGN: Prospective study. METHODS: Patients 50 years and older undergoing major head and neck cancer surgery between 2011 and 2015 preoperatively completed Fried's Frailty Index, Barthel Index, Lawton-Brody questionnaire and Vulnerable Elders Survey-13. Primary outcome measures were postoperative complications and LOS, which were analyzed using multivariable logistic and linear regression models. RESULTS: There were 274 patients recruited (105 aged 50-64 and 169 aged 65 and older). Of these, 119, 132, and 23 were defined as non-frail, pre-frail, and frail, respectively. Frailty score and functional measures were not predictors of overall complications. In multivariable models, frailty score (odds ratio [OR] = 1.36; 95% confidence interval [CI], 1.04-1.78, P = .025) was a predictor of medical complications and Clavien-Dindo Grade III and higher complications independent of age and comorbidity. Higher frailty score (β = 1.07; 95% CI, 1.02-1.12, P = .0025) and less independence on the Lawton Brody (β = -0.08; 95% CI, -0.11 to -0.05, P < .001) and Barthel Index (β = -0.12; 95% CI, -0.19 to -0.06, P < .001) were predictors of increased LOS. CONCLUSIONS: Frailty was a predictor of type and severity of complications. Both frailty and measures of independence in activities of daily living were independent predictors of LOS. Frailty and functional assessment can help surgeons identify patients at risk of adverse postoperative outcomes and thus aid in counselling patients as well as identifying patients that may benefit from comprehensive geriatric assessment and targeted interventions. LEVEL OF EVIDENCE: Prognosis study 2b Laryngoscope, 130:E340-E345, 2020.
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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 | 0.000 | 0.000 |
| 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".