4 mA tDCS improves walking ability in PSP: A case study (P9-11.012)
Bibliographic record
Abstract
Objective: The objective is to examine if tDCS can improve gait speed in people living with Progressive Supranuclear Palsy (PSP). Background: There is no current treatment for PSP. Neuromodulation with transcranial direct current stimulation (tDCS) works through modulating spontaneous activity of neural regions receiving electrical stimulation. The success of tDCS in individuals with motor-related diseases (Parkinson’s Disease and Stroke) indicates that it might be beneficial to individuals with PSP. Our lab (Chertkow-Roncero) has modeled electrical flow, finding that with two anode electrodes placed over the left and right deltoid muscles, and two cathode electrodes placed just ahead of the motor cortex on both sides (C3 & C4), electrical stimulation occurs in midbrain and subthalamic regions. Using this PSP montage, a study from our lab has looked at the effects of tDCS on motor function in PSP, finding improvement persisting for a month. Design/Methods: An 84 year old lady with moderately severe PSP for 5 years and dependent on a walker, underwent two rounds of stimulation using the PSP montage, two months apart. Each round consisted of twelve tDCS sessions over three weeks. In the first week of both rounds, sham tDCS was given to establish a pre-stimulation baseline, while one of two real tDCS montages was given in weeks two and three of each round. tDCS was given at an intensity of 4 mA. Primary outcome measure was gait time which is the time required to walk four lengths of the gait mat(24 meters). Results: Significant improvement in gait time compared to baseline (26.84% improvement immediately after second round and 18.82% at 2 weeks post-stimulation) was noted after the second round. In both the rounds, she demonstrated improvement in cadence, stride length and stride velocity. Conclusions: These results suggest tDCS can provide a significant improvement in the walking ability of people living with PSP. Disclosure: Dr. Lahiri has nothing to disclose. Dr. Roncero has nothing to disclose. Miss Zhang has nothing to disclose. The institution of Dr. Chertkow has received research support from Hoffmann-La Roche Limited. The institution of Dr. Chertkow has received research support from Anavex Life Sciences. The institution of Dr. Chertkow has received research support from Alector Co.. The institution of Dr. Chertkow has received research support from Lilly.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".