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Record W2463647692 · doi:10.3233/jhd-160192

Health Care Delivery Practices in Huntington’s Disease Specialty Clinics: An International Survey

2016· article· en· W2463647692 on OpenAlexaff
Jan C. Frich, D. O. Rae, Richard Roxburgh, Z Miedzybrodzka, Mary Edmondson, Erika Bjorklund Pope, LaVonne Goodman, Mônica Santoro Haddad, Joe Giuliano, Eugene C. Nelson, Mark Guttman, Martha Nance

Bibliographic record

VenueJournal of Huntington s Disease · 2016
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsCentre for Movement Disorders
FundersCHDI Foundation
KeywordsHuntington's diseaseSpecialtyFamily medicineHealth care deliveryMedicineDiseaseHealthcare deliveryHealth carePolitical scienceInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Little is known about the organization of clinical services for Huntington's disease (HD). OBJECTIVE: To describe how health care services are organized and delivered in HD-clinics taking part in or eligible for the Enroll-HD study. METHODS: In 2014, a 69-item survey was administered to sites taking part in or eligible for the Enroll-HD study. RESULTS: Of 231 sites surveyed, 121 (52.2%) sites in Europe, North America, Latin America, and Oceania responded. Most sites in the sample serve large populations, with 61.1% serving more than 1.5 million people, and a further 33% serving >500,000. Almost all (86.0%) centers see patients from outside their region. The majority of centers (59.7%) follow 50-199 patients, 21.9% care for more than 200. Most centers provide care in all stages of HD, and nearly all review pre-symptomatic cases. Multidisciplinary case reviews are offered in 54.5% of sites, with outreach clinics offered by 48.1%. Videoconferencing and telemedicine are used by 23.6%. Separate consultations for caregivers are offered in more than half of the centers. Most centers (70.4%) report following published guidelines or local care pathways for HD. CONCLUSIONS: Most centers serve a large population and use a multidisciplinary approach. The survey gives insight into factors underpinning HD service delivery globally. There is a need for more in-depth studies of clinical practice to understand how services are organized and how such features may be associated with quality of care.

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)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.065
GPT teacher head0.372
Teacher spread0.306 · 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 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

Citations41
Published2016
Admission routes1
Has abstractyes

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