Algorithm to predict postoperative complications in oropharyngeal and oral cavity carcinoma
Bibliographic record
Abstract
BACKGROUND: Preoperative data in patients with oral cavity/oropharyngeal cancer may predict postoperative complications that may modify therapeutic choices and improve patient care. METHOD: We reviewed 320 consecutive patients with oral cavity/oropharyngeal cancer, operated on 2003 through 2006 at the European Institute of Oncology. By multivariate analysis of preoperative patient and tumor characteristics, we developed an algorithm to predict postoperative complications. We tested the algorithm on a new series of 307 patients operated on 2007 through 2010. RESULTS: The final algorithm used to produce a nomogram was comprised of: alcohol consumption (p = .01), site of primary (p = .03), interaction of clinical T classification to sex (p = .007), and type of neck dissection (p < .0001). The algorithm had good ability to predict complications (concordance index [c-index] 0.74) in the new series. CONCLUSION: The nomogram accurately predicts presurgical risk of postoperative local/systemic complications in patients with oral cavity/oropharyngeal cancer and can be used to adapt therapy to patient characteristics, optimize ward admissions, and improve care.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".