P.084 Single-centre follow-up of TYRX Antibiotic Envelope for neuromodulation unit implantation
Bibliographic record
Abstract
Background: Studies have placed the rate of infection associated with neuromodulation units to be up to 20%. We present our experience with the TYRX absorbable antibiotic envelope. Our length of follow-up adds to the body of evidence around the use of antibiotic envelops. Methods: We conducted a retrospective chart review of patients referred to our center for either new implantation or revision of neuromodulation units between July 2014 and September 2016. Consecutive cases were included for analysis. We included a control group of consecutive patients with neuromodulation units placed immediately prior to our experience with the TYRX envelopes for comparison Results: Between July 2014 and September 2016, 76 patients had 81 instances of neuromodulation unit insertion. All patients received the TYRX antibiotic envelope. There were no incidences of infection involving antibiotic envelope-containing implants over an average follow-up period of 11 months. In 77 consecutive cases of neuromodulation unit implantation prior to usage of the antibiotic pouch, there were 4 instances of infection (5.2%). Conclusions: Our single center experience demonstrates a significant drop in the rate of infections with the use of an antibiotic envelope for neuromodulation unit implantation. We consider the routine use of the envelope to be a cost-effective method of infection avoidance.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".