Predictors of Complications in Patients Receiving Head and Neck Free Flap Reconstructive Procedures
Bibliographic record
Abstract
Objective To (1) determine the overall complication rate, wound healing, and wound infection complications and (2) identify preoperative, intraoperative, and postoperative predictors of these complications. Study Design Case series with chart review. Setting Tertiary academic cancer hospital. Subjects and Methods All head and neck free flap patients at The Ohio State University (2006-2012) were assessed. Multivariable logistic regression assessed the impact of patient factors, flap and wound factors, and intraoperative factors on the aforementioned quality metric outcomes. Results Of the 515 patients identified, 54% had a complication predicted by longer operating room (OR) time, higher comorbidity index, and oral cavity and pharyngeal tumor sites. Predictors of wound-healing complications (15%) were longer OR time, volume of crystalloid given intraoperatively, and oral cavity and pharyngeal tumor sites. Predictors of wound infection (12%) were younger age, diabetes mellitus, and malnutrition. Conclusions Wound healing and infectious complications account for most complications in patients with head and neck cancer undergoing free flap reconstruction. Clean contaminated wounds are a significant predictor of wound complications. Advanced OR time, advanced age, and comorbidity status, including diabetes mellitus and malnutrition, are other important predictors. Crystalloid administration is also an important predictor of wound-healing complications, and this warrants further study.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".