Risk of Surgical Site Infection in Posterior Spine Surgery Using Different Closing Techniques: A Retrospective Study of Two Neurosurgical Centers
Bibliographic record
Abstract
Objectives: To determine whether a closed dressing protocol reduces the surgical site infections (SSI) rate compared to conventional closing techniques. Methods: Patients who underwent lumbar spine surgery at two neurosurgical centers were retrospectively included from June 2015 to December 2019. Data on patients, general risk factors, and surgical risk factors for SSI were collected. Patients were subdivided into two groups: a Closed Protocol where the Dermabond® ± Prineo® dressing system was used, and a Conventional Protocol, namely sutures or staples. Statistical analysis was undertaken to compare the infection rates among the different closure techniques. Results: Altogether, 672 patients were included. In the whole cohort, 157 (23.36%) underwent skin closure with staples, 122 (18.15%) with sutures, 98 (14.58%) with intracutaneous sutures, 78 (11.61%) with Dermabond®, and 217 (32.29%) with Demabond® + Prineo®. The overall infection rate was 2.23% (n = 15). Skin suture had the highest infection rate (4.10%), while the lowest was Dermabond® (1.28%) and Dermabond® + Prineo® (1.4%), though the difference was not significant. Risk factors for SSI included higher BMI (29.46 kg/m2 vs. 26.96 kg/m2, p = 0.044), other sites infection (20.00% vs. 2.38%, p = 0.004), and a higher national nosocomial infections surveillance score (p = 0.003). Conclusions: This study showed that a closed protocol with the use of adhesive dressing with or without mesh had a slight tendency to lower infection rates compared to conventional protocol with sutures or staples, although no statistically significant difference was found between the closure techniques. Larger randomized studies are needed to investigate this potential benefit avoiding selection bias.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".