Evaluation of Sarcopenia in Older Patients Undergoing Head and Neck Cancer Surgery
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Sarcopenia is a hallmark of aging and its identification may help predict adverse postoperative events in patients undergoing head and neck surgery. The study objective was to assess the relationship between sarcopenia and postoperative complications and length of stay in patients undergoing major head and neck cancer surgery. STUDY DESIGN: Prospective cohort study. METHODS: A prospective cohort study was performed of patients 50 years and older undergoing major head and neck surgery. Sarcopenia was defined as low muscle mass (determined by neck muscle cross-sectional imaging) with either low muscle strength (grip strength) or low muscle performance (timed walk test). Logistic regression was applied on binary outcomes, and linear regression was used for log-transformed length of hospital stay (LOS). Univariate and multivariate analyses were performed. RESULTS: Of the 251 patients enrolled, pre-sarcopenia was present in 34.9% (n = 87) and sarcopenia in 15.6% (n = 39) of patients. Patients with sarcopenia were more likely to be older (P = .001), female (P = .001), have a lower body mass index (P = .001), and lower preoperative hemoglobin (P < .001). On univariate analysis, the presence and severity of sarcopenia was associated with the development of medical complications (P = .029), higher grade of complications (P = .032), LOS (P = .015), and overall survival (P = .001). On multivariate analysis, sarcopenia was associated with a longer LOS (β = 0.32 [95% CI: 0.19-0.45], P < .001) and worse overall survival (HR = 2.21 [95% CI: 1.01-4.23], P = .017). CONCLUSIONS: Sarcopenia may aid in the prediction of prolonged hospital stay and death in patients who are candidates for major head and neck surgery. LEVEL OF EVIDENCE: 3 Laryngoscope, 132:356-363, 2022.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".