Alexithymia in patients with Parkinson’s disease treated with DBS of the subthalamic nucleus: a case-control study
Bibliographic record
Abstract
OBJECTIVES: To evaluate the effect of deep brain stimulation of the subthalamic nucleus (STN-DBS) on alexithymia, a deficit in affective regulation, comparing patients with Parkinson's disease (PD) submitted to STN-DBS (DBS group) to PD patients not yet treated with STN-DBS (pre-DBS group) and to healthy participants (C group). METHODS: We recruited 27 consecutive STN-DBS PD patients, 38 consecutive pre-DBS patients and 27 healthy participants. Patients were assessed for alexithymia (Toronto Alexithymia Scale), depression, [beck depression inventory (BDI)], and cognitive functions (reasoning, memory, attentional, and executive tests). RESULTS: The DBS patients performed worse than the pre-DBS patients in the corsi's block-tapping test, in the phonemic fluency task and in the Frontal Assessment Battery. Around 30% of DBS (29.6%) and pre-DBS (31.6%) patients resulted alexithymic, compared with 14.8% in the C group. The results pointed out significantly higher alexithymia scores in both the DBS and pre-DBS groups compared with the C group, while no difference emerged between the DBS and pre-DBS groups. Pre-DBS group showed a significantly higher BDI score than the C group, while DBS group did not. CONCLUSION: Although the results suggest that STN-DBS does not affect alexithymia, both the DBS and pre-DBS patients reported higher prevalence (about 30%) of alexithymia than did healthy subjects (14.8%).
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".