Point-of-Care Programming for Neuromodulation
Bibliographic record
Abstract
BACKGROUND: The expansion of neuromodulation and its indications has resulted in hundreds of thousands of patients with implanted devices worldwide. Because all patients require programming, this growth has created a heavy burden on neuromodulation centers and patients. Remote point-of-care programming may provide patients with real-time access to neuromodulation expertise in their communities. OBJECTIVE: To test the feasibility of remotely programming a neuromodulation device using a remote-presence robot and to determine the ability of an expert programmer to telementor a nonexpert in programming the device. METHODS: A remote-presence robot (RP-7) was used for remote programming. Twenty patients were randomly assigned to either conventional programming or a robotic session. The expert remotely mentored 10 nurses with no previous experience to program the devices of patients assigned to the remote-presence sessions. Accuracy of programming, adverse events, and satisfaction scores for all participants were assessed. RESULTS: There was no difference in the accuracy or clinical outcomes of programming between the standard and remote-presence sessions. No adverse events occurred in any session. The patients, nurses, and the expert programmer expressed high satisfaction scores with the remote-presence sessions. CONCLUSION: This study establishes the proof-of-principle that remote programming of neuromodulation devices using telepresence and expert telementoring of an individual with no previous experience to accurately program a device is feasible. We envision a time in the future when patients with implanted devices will have real-time access to neuromodulation expertise from the comfort of their own home.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".