Surgical Site Infection Affects Length of Stay After Complex Head and Neck Procedures
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Quality improvement (QI) initiatives emphasize a need for reduction in hospital length of stay (LOS). We sought to determine the impact of surgical site infections (SSIs) on LOS after complex head and neck surgery (HNS). STUDY DESIGN: Retrospective cohort analysis. METHODS: An analysis of the American College of Surgeons National Surgical Quality Improvement Program was undertaken. All adult patients undergoing complex HNS from 2005 to 2016 were included in the analysis. Our main outcomes were SSI incidence and increase in hospital LOS attributable to SSI. RESULTS: Of 4,014 patients identified, 16.5% developed SSI. History of smoking, diabetes, preoperative wound infection, contaminated or dirty wound classes, and prolonged operative time were found to significantly predict postoperative SSI. Adjusting for significant pre- and postoperative factors, SSI was associated with significantly increased LOS (hazard ratio = 0.486, 95% confidence interval: 0.419-0.522). CONCLUSIONS: SSI following complex HNS is associated with significantly increased hospital LOS. This result supports the need for institutional QI strategies that target SSIs after head and neck procedures in an effort to provide the highest quality care at the lowest possible cost. Our analysis identifies risk factors that can allow identification of patients at high risk of SSI and prolonged hospitalization. LEVEL OF EVIDENCE: 2b Laryngoscope, 2020.
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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 | 0.010 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".