Analysis of readmissions after transoral robotic surgery for oropharyngeal squamous cell carcinoma
Bibliographic record
Abstract
BACKGROUND: As transoral robotic surgery (TORS) is being increasingly used to treat patients with oropharyngeal squamous cell carcinoma (OPSCC), there is an interest in determining contributors to readmission. METHODS: We conducted this retrospective multivariate analysis modeling 30-day readmission using the Nationwide Readmissions Database (2012-2014). RESULTS: Of 950 patients, 117 (12.3%) were readmitted. Hemorrhage and diet/aspiration accounted for 32.5% and 19.7% of readmissions, respectively. Of those readmitted, 23.1% required operative bleeding control, 11.1% required transfusion, 1.7% required tracheostomy, and 18.8% required gastrostomies. Those readmitted were older (mean 63.2 years, SD 9.5 vs 60.9 mean years, SD 10.3) and had longer hospitalizations (mean 5.7 days, SD 6.8 vs mean 4.3 days, SD 4.1) and higher rates of aspiration/pneumonia (9.4% vs 2.4%, P < .01) on index admission. Multivariate analysis demonstrated that aspiration/pneumonia on index admission was independently associated with readmission (OR 3.128, 95% CI 1.178-8.302). CONCLUSIONS: Of the patients 12.3% were readmitted within 30 days with hemorrhage and diet complications as significant contributors.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".