Shielded Battery Syndrome: A New Hardware Complication of Deep Brain Stimulation
Bibliographic record
Abstract
BACKGROUND: Deep brain stimulation hardware is constantly advancing. The last few years have seen the introduction of rechargeable cell technology into the implanted pulse generator design, allowing for longer battery life and fewer replacement operations. The Medtronic® system requires an additional pocket adaptor when revising a non-rechargeable battery such as their Kinetra® to their rechargeable Activa® RC. This additional hardware item can, if it migrates superficially, become an impediment to the recharging of the battery and negate the intended technological advance. AIM: To report the emergence of the 'shielded battery syndrome', which has not been previously described. METHODS: We reviewed our deep brain stimulation database to identify cases of recharging difficulties reported by patients with Activa RC implanted pulse generators. RESULTS: Two cases of shielded battery syndrome were identified. The first required surgery to reposition the adaptor to the deep aspect of the subcutaneous pocket. In the second case, it was possible to perform external manual manipulation to restore the adaptor to its original position deep to the battery. CONCLUSIONS: We describe strategies to minimise the occurrence of the shielded battery syndrome and advise vigilance in all patients who experience difficulty with recharging after replacement surgery of this type for the implanted pulse generator.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".