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Record W1968772654 · doi:10.1159/000075161

Longevity of Batteries in Internal Pulse Generators Used for Deep Brain Stimulation

2003· article· en· W1968772654 on OpenAlexaff
Mohammed Binmahfoodh, Clement Hamani, Elspeth Sime, Andrés M. Lozano

Bibliographic record

VenueStereotactic and Functional Neurosurgery · 2003
Typearticle
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsToronto Western HospitalUniversity of Toronto
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsBattery (electricity)Deep brain stimulationLongevityMedicineLife spanFunctional electrical stimulationParkinson's diseaseStimulationDiseaseGerontologyInternal medicine

Abstract

fetched live from OpenAlex

The longevity of batteries in internal pulse generators (IPGs) used clinically for deep brain stimulation is not known. We conducted a study to assess the life span of these batteries. From 1993 to 2000, 163 single-channel batteries were surgically implanted in our institution. The electrical settings utilized in patients who had battery failures were assessed and the total electrical energy delivered (TEED) was calculated and correlated with battery longevity. Fourteen IPGs had battery failure requiring replacement. The median life span of the batteries was 45 months. Batteries with high energy consumption as assessed by TEED had a reduced life span (r = -0.82, p < or = 0.001). Patients with essential tremor who required battery replacement needed higher settings to control their symptoms and therefore presented a shorter battery life when compared to patients with Parkinson's disease. In our series of patients who needed battery replacement, battery longevity varied with stimulation parameters but was longer than expected from the manufacturer's specifications.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.031
GPT teacher head0.265
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations84
Published2003
Admission routes1
Has abstractyes

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