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Record W4280541581 · doi:10.3390/jpm12050804

Measuring Quality of Life in Parkinson’s Disease—A Call to Rethink Conceptualizations and Assessments

2022· article· en· W4280541581 on OpenAlexfundno aff
Maria Stührenberg, Carolin S. Berghäuser, Marlena van Munster, Anna J. Pedrosa Carrasco, David J. Pedrosa

Bibliographic record

VenueJournal of Personalized Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
FundersInstituto de Salud Carlos IIIMedical Research CouncilBundesministerium für Bildung und ForschungCanadian Institutes of Health ResearchEU Joint Programme – Neurodegenerative Disease Research
KeywordsParkinson's diseaseQuality of life (healthcare)DiseaseMedicineQuality (philosophy)PsychologyGerontologyPhysical medicine and rehabilitationPathologyNursingEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

Parkinson's disease (PD) is a chronic condition that considerably impacts the perception of quality of life (QoL) in both patients and their caregivers. Modern therapeutic approaches and social efforts strive at maintaining and promoting QoL. It has emerged as a fundamental parameter for clinical follow-up and poses one of the most important endpoints in scientific and economic evaluations of new care models. It is therefore of utmost importance to grasp concepts of QoL in a meaningful way. However, when taking a look at the origin of our modern understanding of QoL and existing methods for its measurement in PD patients, some aspects seem to lack sufficient appreciation. This article elaborates on how the perception of health and QoL have changed over time and discuss whether current understandings of both are reflected in the most commonly applied assessment methods for people with PD.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.747

Codex and Gemma teacher scores by category

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

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 head0.103
GPT teacher head0.370
Teacher spread0.267 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations12
Published2022
Admission routes1
Has abstractyes

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