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Record W4280637761 · doi:10.3390/jpm12050813

Personalized Care in Late-Stage Parkinson’s Disease: Challenges and Opportunities

2022· review· en· W4280637761 on OpenAlexaff
Margherita Fabbri, Miguel Coelho, Michela Garon, Roberta Biundo, Tiago Mestre, Angelo Antonini

Bibliographic record

VenueJournal of Personalized Medicine · 2022
Typereview
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersAgence Nationale de la Recherche
KeywordsMedicineDiseaseParkinson's diseasePalliative careDementiaPsychological interventionHealth careIntensive care medicinePhysical medicine and rehabilitationPhysical therapyNursing

Abstract

fetched live from OpenAlex

Late-stage Parkinson's disease (LSPD) patients are highly dependent on activities of daily living and require significant medical needs. In LSPD, there is a significant caregiver burden and greater health economic impact compared to earlier PD stages. The clinical presentation in LSPD is dominated by motor and non-motor symptoms (NMS) that most of the time have a sub-optimal to no response to dopaminergic treatment, especially when dementia is present. Non-pharmacological interventions, including physiotherapy, cognitive stimulation, speech, occupational therapy, and a specialized PD nurse, assume a key role in LSPD to mitigate the impact of disease milestones or prevent acute clinical worsening and optimize the management of troublesome NMS. However, the feasibility of these approaches is limited by patients' cognitive impairment and the difficulty in delivering care at home. The present care challenge for LSPD is the ability to offer a person-centered, home-delivered palliative care model based on Advanced Care Planning. An ongoing European multicentric project, PD_Pal, aims to address this challenge.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.947
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.154
GPT teacher head0.362
Teacher spread0.208 · 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 designNot applicable
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

Citations22
Published2022
Admission routes1
Has abstractyes

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