Long-term Hardware-related Complications of Deep Brain Stimulation
Bibliographic record
Abstract
OBJECTIVE: To determine the incidence of long-term hardware-related complications of deep brain stimulation (DBS). METHODS: The study design is a retrospective chart review of a single-surgeon, single-institution experience with DBS in 84 consecutive cases from 1993 to 1999. Only patients with a minimum follow-up of 1 year were considered. Five patients were excluded because trial stimulation failed to achieve pain relief (n = 4) or because the procedure was aborted owing to hemorrhage (n = 1). Seventy-nine patients received 124 permanent DBS electrode implants. RESULTS: The mean follow-up period was 33 months, and the cumulative follow-up time was 217 patient-years or 310 electrode-years. Overall, 20 patients (25.3%) had 26 hardware-related complications involving 23 (18.5%) of the electrodes. There were 4 lead fractures, 4 lead migrations, 3 short or open circuits, 12 erosions and/or infections, 2 foreign body reactions, and one cerebrospinal fluid leak. The hardware-related complication rate per electrode-year was 8.4%. The most common complications were related to the electrode connectors. A significant finding was a high number of complications involving erosions or infections, which occurred in 7 of 12 instances as a late complication (beyond 12 mo). CONCLUSION: Long-term follow-up reveals that hardware-related complications occur in a significant number of patients. Factors that lead to such complications must be identified and addressed to maximize the important benefits of DBS therapy.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".