P.098 Antibacterial envelopes prevent post-operative infections in neuromodulation surgery
Bibliographic record
Abstract
Background: Neuromodulation unit placement can provide efficacious control of many neurological conditions. They are high risk for infection with a historic infection rate as high as 10%. Treatment of infection requires surgical removal and a long course of systemic antibiotics. At our center, one surgeon uses antibacterial envelopes with all implanted neuromodulation devices. Methods: We conducted a retrospective cohort study of consecutive implantable pulse generator (IPG) and intrathecal pump unit implantation with an antibacterial envelope at our center. This cohort was then compared to a historical cohort of consecutive patients undergoing IPG or pump placement or revision prior to the use of the envelopes. Results: IPG: There were 18 (11.9%) class I infections in the pre-envelope cohort compared with 5 (2.1%) in the post-envelope cohort. The absolute risk reduction (ARR) with the use of antibacterial envelopes was 9.85% (95% confidence interval (CI) 4.3-15.4%, p<0.01). Pump: There were 6 (14.6%) class I infections in the pre-envelope cohort compared with 1 (1.7%) in the post-envelope cohort. The ARR with the use of antibacterial envelopes was 12.9% (95% confidence interval 1.6-24.3, p<0.05). Conclusions: Based on our results, we recommend usage of antibacterial envelopes to reduce infection rates in neuromodulation surgery. Further study is needed.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".