Effect of High-Frequency Repetitive Transcranial Magnetic Stimulation on Visual Selective Attention in Male Patients With Alcohol Use Disorder After the Acute Withdrawal
Bibliographic record
Abstract
Objective: To investigate the effect of high-frequency repetitive transcranial magnetic stimulation (rTMS) on attention cue reactivity in male patients with alcohol use disorder (AUD) after acute withdrawal. Methods: A total of 90 male patients with AUD who were hospitalized were enrolled and divided into study and waiting groups by a random number table. During the study, 18 patients dropped out. After the alcohol withdrawal symptoms were eliminated, the study group received high-frequency rTMS at 10 Hz for 14 consecutive days, and the waiting group was administrated by sham rTMS. All subjects were evaluated for attention cue reactivity, impulsiveness, cognitive function by oddball paradigm, Barratt Impulsiveness Scale version II (BIS-II), and the Montreal Cognitive Assessment (MoCA) at baseline and after true or sham rTMS. Results: 1. There was no significant difference between the study and the waiting groups regarding the drinking level, cognition level, and demographic data at baseline. 2. In the oddball paradigm, both for alcohol-related and non-alcohol-related cues, the response times were significantly shorter in the study group after rTMS treatment than in the waiting-for-treatment group, either between the two groups or within the study group. There was no significant difference in the accuracy rate for alcohol-related and non-alcohol-related cues between the two groups or within the study group after rTMS intervention. 3. The total score of MoCA was significantly increased, and the total score of BIS-II was significantly decreased in the study group after rTMS treatment, either between the two groups or within the study group. Conclusion: The results suggested that high-frequency rTMS could improve the attention bias of alcohol-related cues and impulsivity for patients with AUD.
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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 | 0.000 |
| 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) | 0.001 | 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".