Predictors of Surgical Site Infection in Pediatric Cochlear Implantation
Bibliographic record
Abstract
OBJECTIVE: To determine risk factors for surgical site infection (SSI) after cochlear implantation (CI) in pediatric patients. STUDY DESIGN: Case-control study. SETTING: A total of 150 hospitals contributing data to the ACS-NSQIP Pediatric database (American College of Surgeons National Surgical Quality Improvement Program) in North America and worldwide. METHODS: Pediatric patients (aged <18 years) undergoing CI during the years 2012 to 2017 were identified in the ACS-NSQIP Pediatric database. Uni- and multivariable logistic regression analyses were used to determine the odds ratios (ORs) of SSI (including superficial incisional, deep incisional, organ/space) occurring up to 30 days postoperatively. RESULTS: A total of 79 SSIs occurred over a 5-year period (n = 5146). Longer operative time significantly increased the odds of SSI (OR, 1.965; 95% CI, 1.205-3.289). Younger age was also found to raise the odds of SSI, with decreased odds associated with each 6-month increase in age (OR, 0.887; 95% CI, 0.814-0.958). CONCLUSION: Longer operative time and younger age appear to significantly increase the odds of SSI in pediatric CI. Body mass index, recent steroid use, American Society of Anesthesiologists class, bilateral vs unilateral implantation, and hospital length of stay do not appear to significantly influence SSI risk. These findings must be interpreted in the context of the limitations inherent to adverse events reporting, which are mitigated by the stringent manner of data collection by the ACS-NSQIP, and those inherent to the definition of SSI. Future prospective studies should investigate the impact of reducing operative time on the risk of SSI and other complications in pediatric CI.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".