Incidence of surgical site infections in children: active surveillance in an Italian academic children's hospital.
Bibliographic record
Abstract
BACKGROUND: Surgical Site Infections (SSIs) account for 16-34% of all health-care associated infections. This study aimed to assess the incidence rate of SSIs in children who underwent surgical procedures in an academic children's hospital in Italy. STUDY DESIGN: Prospective cohort study. METHODS: We actively followed-up 0-17 year old children at 30 days of surgical procedures without implants conducted during one index week per quarter, from the second quarter of 2014, to the first quarter of 2016 (8 index weeks in total). Follow up data were collected by telephone interview, or derived by clinical records if patients were still hospitalized. SSIs were defined according to case definitions of Centers for Diseases Control, Atlanta, USA. We calculated cumulative incidence of SSIs per 100 surgical procedures, by patient characteristics, procedure characteristics, and quarter. To investigate variables associated with SSIs, we compared characteristics of procedures with SSIs with those of procedures without SSIs. RESULTS: Over the study period, SSI incidence was 1.0% (19 cases/1,830 surgical procedures). SSI incidence was significantly lower after ear, nose and throat procedures compared to all other procedures, and significantly decreased over time. Duration of surgery was a risk factor for SSIs; patients with SSIs had a significantly longer total length of stay (LOS), due to a prolonged post-operative LOS. CONCLUSION: As reported in adults, this study confirms that SSIs are associated with longer hospitalizations in children. Active surveillance of SSIs is an important component of the overall strategy to reduce the incidence of these infections in children.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".