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Record W4401910143 · doi:10.1155/2024/5551796

Possible Implications of Managing Alexithymia on Quality of Life in Parkinson’s Disease: A Systematic Review

2024· review· en· W4401910143 on OpenAlexaboutno aff
Laura Culicetto, Caterina Formica, Viviana Lo Buono, Dèsiréè Latella, Giuseppa Maresca, Amelia Brigandì, Chiara Sorbera, Giuseppe Di Lorenzo, Angelo Quartarone, Silvia Marino

Bibliographic record

VenueParkinson s Disease · 2024
Typereview
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAlexithymiaParkinson's diseaseDiseaseQuality of life (healthcare)PsychiatryPathologyNursing

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.074
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.071
GPT teacher head0.390
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations7
Published2024
Admission routes1
Has abstractyes

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