Possible Implications of Managing Alexithymia on Quality of Life in Parkinson’s Disease: A Systematic Review
Bibliographic record
Abstract
Alexithymia, characterized by difficulty in recognizing and verbalizing emotions, is reported to be more prevalent in subjects with Parkinson's disease (PD) than in the general population. Although it is one of the nonmotor symptoms of PD, alexithymia is often overlooked in clinical practice. The aim of this systematic review is to investigate the prevalence of alexithymia in PD, assess its impact on quality of life, and explore the rehabilitation approaches for alexithymia. Research articles, selected from PubMed, Scopus, and Web of Science, were limited to those published in English from 2013 to 2023. The search terms combined were "Alexithymia," "Parkinson's disease,", and "Quality of life." Current literature review indicates that alexithymia is commonly assessed using the Toronto Alexithymia Scale (TAS-20), and it is associated with deficits in visuospatial and executive functions. Presently, rehabilitation interventions for alexithymia are scarce, and their effectiveness remains controversial. Future research should focus on developing comprehensive assessments and rehabilitation strategies for emotional processing, considering its significant impact on the quality of life of both patients and caregivers.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".