Reducing Hardware-Related Complications of Deep Brain Stimulation
Bibliographic record
Abstract
BACKGROUND: Deep brain stimulation (DBS) is used increasingly worldwide for the treatment of Parkinson's disease, dystonia, tremor and pain. As with any implanted system, however, DBS introduces a new series of problems related to its hardware. Infection, malfunction and lead migration or fracture may increase patient morbidity and should be considered when evaluating the risk/benefit ratio of this therapy. This work highlights several factors felt to increase DBS hardware complications. METHODS: The authors undertook a prospective analysis of their patients receiving this therapy in two Canadian centres, over a four-year period. RESULTS: One hundred and forty-four patients received 204 permanent electrode implants. The average follow-up duration was 24 months. Complications related to the DBS hardware were seen in 11 patients (7.6%). There were two lead fractures (1.4%) and nine infections (6.2%) including two erosions (1.4%). There was a significantly greater risk of infection in patients who underwent staged procedures with externalization. In patients with straight scalp incisions, the rate of infection was higher than that seen with curved incisions. CONCLUSION: Hardware complications were not common. A period of externalization of the electrodes for a stimulation trial was associated with an increased infection rate. It is also possible that a straight scalp incision instead of curvilinear incision may lead to an increase in the rate of infection. With a clear understanding of the accepted DBS device indications and their potential complications, patients may make a truly informed decision about DBS technology.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".