human Vim-DBS data of membrane potential
Bibliographic record
Abstract
The data protocols are from Milosevic et al. (2021) (L. Milosevic et al., “A theoretical framework for the site-specific and frequency-dependent neuronal effects of deep brain stimulation,” Brain Stimul, vol. 14, no. 4, pp. 807–821, 2021, doi: 10.1016/j.brs.2021.04.022.) The data were recorded from the thalamic ventral intermediate nucleus (Vim) in human patients receiving DBS for treating essential tremor. Microelectrodes were used to both deliver DBS and perform single-unit recordings. DBS was delivered using 100 µA and symmetric 0.3ms biphasic pulses (150µs cathodal followed by 150 µs anodal). In this work, we used the entire dataset of single-unit recordings of the neurons in the thalamic ventral intermediate nucleus (Vim) of essential tremor patients, during various DBS frequencies (5 to 200 Hz) in Vim. The single-unit recordings during {5, 10, 20, 30, 50, 100, and 200 Hz} Vim-DBS are of length {10, 5, 3, 2, 1, 5, and 2 s}, respectively; for each frequency of DBS, we did 5 to 8 recordings in different patients (total number of patients = 19). To obtain spikes from the single-unit recordings, we did offline analysis and spike template matching. For each single-unit recording, all the narrow stimulus artifacts were removed (0.5 ms from the onset of a DBS pulse). Then the recordings were high pass filtered (≥300 Hz) to better isolate the spikes, which were identified by the template matching using a principal component analysis method in Spike2 (Cambridge Electronic Design, UK).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".