Gram-Negative Surgical Site Infections After 989 Spinal Fusion Procedures: Associated Factors and the Role of Gram-Negative Prophylactic Antibiotic Coverage
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: To identify, analyze, and report the patient- and procedure-related factors associated with surgical site infection (SSI) after spinal fusion (SF) surgery. METHODS: We included any SSI-SF from January 2013 to September 2015. A total of 989 spine surgeries that required instrumentation were performed. RESULTS: Twenty-four out of 989 (2.43%) patients presented with SSI. More than half of the SSI cases (54%) got infected with either exclusively gram-negative bacteria or a combination of gram-negative and gram-positive bacteria; 9.1% of the surgeries involved the sacral spine (90 out of 989 patients). SSI in long constructs (more than 3 levels) was performed in 66.7% compared with 33.3% with short constructs; 87.5 % of the reported SSI (21 patients) were done through a posterior approach. Of patients who had SSI, 87.5% received prophylactic antibiotics, 92% were operated on during the daytime shift, 50% required blood transfusion, and 79% required surgical debridement. Four patients out of 24 patients died (17%) due to unrelated SSI complications. CONCLUSIONS: The overall incidence of gram-negative infections after long SFs remains low in our study population. Despite this low overall incidence, our results demonstrate a relative higher incidence of gram-negative SSIs in surgeries involving more than 3 spinal levels and for all those involving the sacral spine. We propose that there may be a potential benefit of gram-negative prophylactic antibiotic coverage in patients falling in either 1 of these categories. Further multivariate analysis and/or randomized studies may be necessary to confirm our results. LEVEL OF EVIDENCE: 3.
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.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 it