P2‐318: Tdcs stimulation alongside picture training improves naming scores in anomic dementia patients
Bibliographic record
Abstract
Individuals with Alzheimer Disease (AD) and Frontotemporal Dementia (FTD) often show anomia, failure to name common objects. These semantic deficits appear to have two distinct patterns: 1) a semantic control deficit (difficulty in accessing and using the meaning of the object) or 2) a semantic storage deficit (lost memory for the object concept as well as related knowledge). Transcranial direct current stimulation (tDCS) as a treatment for improving naming abilities in these patient groups. Using a double-blind design, two FTD patients, both with semantic control deficits, and two AD patients, both with semantic storage deficits, were first asked to name a set of images (n = 120) from two separate lists balanced for familiarity; hereafter called trained (n = 60) and untrained (n = 60). Next, one patient in each sub-group (control vs. storage) received 10 sessions of anode stimulation to the inferior parietal lobe (IPL) area for 30 minutes at an intensity of 2 ma over the course of 3 weeks. The other patient in each group received sham stimulation. During these stimulation sessions, subjects were asked to name images from the trained list; errors were then discussed, and patients were again asked to name the incorrectly named images until they could correctly do so within cycles of five items. During the final tDCS session, both lists (trained and untrained) were again presented. For the untrained items, with real tDCS stimulation, our FTD control-deficit subject improved 10%, while our AD storage deficit subject improved 7%. In contrast, with sham stimulation, there was no change in our AD storage deficit subject, and the second FTD control-deficit subject was actually 10% worse. On the trained items, with real stimulation, our FTD subject improved 20% and obtained a perfect score, while the FTD sham counter-part showed no improvement. Finally, the AD storage deficit subject receiving real tDCS stimulation showed a small improvement on trained items (4%), in contrast to the AD sham counter-part that was 2% worse.
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.
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 | 0.000 |
| 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) | 0.003 | 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".