Investigation into the effect of transcranial direct current stimulation on cardiac pacemakers
Bibliographic record
Abstract
BACKGROUND: Studies investigating the therapeutic applications of transcranial direct current stimulation (tDCS) in the treatment of age-related neurodegenerative disease have been promising. However, exclusion criteria for these studies invariably disqualify patients implanted with internal cardiac pacemakers, citing safety concerns. Because the majority of cardiac pacemaker implantees are over 65, this criterion may limit candidacy for tDCS based research and/or treatment of age-related neurodegenerative disease. OBJECTIVE/HYPOTHESIS: We will test the hypothesis that tDCS impacts pacemaker function. Strong electrical potentials, such as those generated by external defibrillators (∼500 V, ∼10 A), are known to occasionally damage pacemaker circuitry and software, but it seems unlikely tDCS would damage a pacemaker because it involves about 1/200th the energy (∼12 V, ∼2 mA) of an external defibrillator. METHODS: We delivered tDCS to seven participants (ages 70-92) with bipolar non-dependent pacemakers and subsequently collected data from the internal memory of the pacemakers to assess the tDCS signal detection, as well as alterations in mode switches, impedance levels, and pacing. Subsequently, similar assessments were carried out in participants who were pacemaker-dependent (ages 89-91). RESULTS: After a review of the recordings, it was found that tDCS had no impact on the non-dependant, as well as the dependent, pacemakers. There were zero mode switches nor any impact on impedance levels. CONCLUSION: Results in this small series of cases found no evidence that tDCS interferes with the function of the pacemakers and suggests tDCS can be delivered to patients equipped with a cardiac pacemaker. Further studies are needed to generalize these results to other pacemakers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".