Post‐acute care use after major head and neck oncologic surgery with microvascular reconstruction
Bibliographic record
Abstract
OBJECTIVES: Post-acute care (PAC) centers, such as skilled nursing facilities, unskilled nursing facilities, lower acuity hospitals, and rehabilitation centers, serve to optimize recovery after acute care hospitalization. We aimed to identify factors associated with PAC utilization among patients undergoing head and neck cancer surgery with microvascular reconstruction because it may be helpful for patient decision making, discharge planning, and resource allocation. METHODS: Retrospective linked analysis of the 2011 to 2015 National Surgical Quality Improvement Program. Eligible patients were identified and stratified by discharge disposition (home or PAC) after their postoperative acute-care hospitalization. After an initial univariate screen of demographic and clinical variables, a multivariable logistic regression analysis was performed modelling discharge to PAC. RESULTS: Of the 1,652 identified patients, 261 (15.8%) were discharged to PAC. Those admitted to PAC were older, had a higher burden of comorbidity, and were more likely to be functionally dependent. They also had longer surgeries, longer hospitalizations, higher rates of reoperation, and higher rates of postoperative complications. After multivariate analysis, factors independently associated with PAC discharge included increasing age (odds ratio [OR] 2.12 per 10-year increase; 95% confidence interval [CI], 1.81-2.48), active smoking status (odds ratio (OR) 1.61; 95% confidence interval (CI), 1.13-2.29), prolonged hospitalization (OR 1.04; 95% CI, 1.02-1.07), and postoperative pulmonary complications (OR 2.02; 95% CI, 1.36-2.99). CONCLUSION: Of the patients undergoing surgery for head and neck cancers with microvascular reconstruction, 15.8% are discharged to PAC. Age, active smoking status, prolonged hospitalization, and postoperative pulmonary complications (vs. comorbidity, functional status, or primary tumor site) are independently associated with discharge to PAC. LEVEL OF EVIDENCE: Level 2c. Laryngoscope, 2532-2538, 2018.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".