A New Rechargeable Device for Deep Brain Stimulation: A Prospective Patient Satisfaction Survey
Bibliographic record
Abstract
BACKGROUND: Deep brain stimulation (DBS) is highly successful in treating Parkinson's disease (PD), dystonia, and essential tremor (ET). Until recently implantable neurostimulators were nonrechargeable, battery-driven devices, with a lifetime of about 3-5 years. This relatively short duration causes problems for patients (e.g. programming and device-use limitations, unpredictable expiration, surgeries to replace depleted batteries). Additionally, these batteries (relatively large with considerable weight) may cause discomfort. To overcome these issues, the first rechargeable DBS device was introduced: smaller, lighter and intended to function for 9 years. METHODS: Of 35 patients implanted with the rechargeable device, 21 (including 8 PD, 10 dystonia, 2 ET) were followed before and 3 months after surgery and completed a systematic survey of satisfaction with the rechargeable device. RESULTS: Overall patient satisfaction was high (83.3 ± 18.3). Dystonia patients tended to have lower satisfaction values for fit and comfort of the system than PD patients. Age was significantly negatively correlated with satisfaction regarding process of battery recharging. CONCLUSIONS: Dystonia patients (generally high-energy consumption, severe problems at the DBS device end-of-life) are good, reliable candidates for a rechargeable DBS system. In PD, younger patients, without signs of dementia and good technical understanding, might have highest benefit.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".