Surgical site infections in neurosurgery
Bibliographic record
Abstract
Background: Surgical site infections in neurosurgery are serious due to their proximity to the central nervous system and their management is a challenge. The aim of our work is to report surgical site infections (SSI) in patients who underwent brain or spinal surgery and to describe their characteristics. Materials and method: We conducted a retrospective study involving patients who underwent surgery in our facility's neurosurgical emergency department over 5 years from January 2015 to December 2019. The data were collected from medical hospital and follow-up records. Results: Fifty-eight cases of SSI were identified out of 2889 operations in total, for a frequency of 2%. The series consisted of 36 men (62.07%) and 22 women (37.93%). The average age was 43.9 years (19-72 years). 46 patients (79.31%) had undergone urgent surgery and 12 patients (20.69%) for delayed surgery. 40 patients (68.97%) had undergone cranial intervention and 18 patients (31.03%) underwent spinal surgery. The identified germ was Staphylococcus aureus in 13 cases (76.48%). Mortality was 13.8% (8 out of 58 cases). Conclusion: The majority of microorganisms that cause the infections contaminate the surgical site intraoperatively. Preventive measures can reduce the rate of surgical site infections.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".