Point-of-Care Programming for Neuromodulation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it