Predictors of morbidity following free flap reconstruction for cancer of the head and neck
Bibliographic record
Abstract
BACKGROUND: Free flap reconstruction of head and neck cancer defects is complex with many factors that influence perioperative complications. The aim was to determine if there was an association between perioperative variables and postoperative outcome. METHODS: We evaluated 185 patients undergoing free flap reconstruction following ablation of head and neck cancer between 1999 and 2001. Demographic, laboratory, surgical and anesthetic variables were analyzed using univariate and multivariable techniques. RESULTS: Ninety-eight patients (53%) developed complications, of which 74 were considered major, giving a major morbidity rate of 40%. Predictors of major complications were increasing patient age, ASA class, and smoking. Predictors of medical complications were ASA class, smoking, age and crystalloid replacement. Predictors of surgical complications were tracheostomy, preoperative hemoglobin, and preoperative radiotherapy. CONCLUSION: Patient age, comorbidity, smoking, preoperative hemoglobin, and perioperative fluid management are potential predictors of postoperative complications following free flap reconstruction for cancer of the head and neck.
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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 | 0.000 |
| 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) | 0.001 | 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".