MétaCan
Menu
Back to cohort
Record W3178639618 · doi:10.1007/s12311-021-01271-6

The Cost of Living with Inherited Ataxia in Ireland

2021· article· en· W3178639618 on OpenAlexaff
Mark Kelly, Petya Bogdanova‐Mihaylova, Joshua Skeens, Sharon Moran, Sorcha Farrelly, Richard A. Walsh, Sinéad M. Murphy

Bibliographic record

VenueThe Cerebellum · 2021
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsTrinity College
Fundersnot available
KeywordsAtaxiaMedicineCohortIndirect costsHealth careNeurologyPsychiatryGerontologyPediatricsInternal medicineBusinessEconomics

Abstract

fetched live from OpenAlex

Inherited ataxias are a heterogenous group of neurodegenerative disorders characterised by progressive impairment of balance and coordination, typically leading to permanent and progressive disability. Diagnosis and management of these disorders incurs a range of direct and indirect financial costs. The aim of this study was to collect individual ataxia-related healthcare resources in a large cohort of individuals with different subtypes of inherited ataxia and calculate the associated cost of illness in the Republic of Ireland. One hundred twenty-nine respondents completed a cross-sectional study on healthcare resource utilisation for progressive ataxia in Ireland. Costs were calculated using a prevalence-based approach and bottom-up methodology. The COI for inherited ataxia in 2016 was €59,993 per person per year. Results were similar between participants with Friedreich's ataxia (FRDA, n = 56), non-FRDA (n = 18) and those with undetermined ataxia (n = 55). Indirect costs, based on productivity losses by participants or caregivers, accounted for 52% of the cost of illness. Inherited ataxia is associated with significant health and social care costs. Further funding for inherited ataxia to ease the financial burden on patients, caregivers and healthcare system and improve standards of care compliance is warranted.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.189

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.025
GPT teacher head0.247
Teacher spread0.222 · 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 designBench or experimental
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

Citations5
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueThe CerebellumSame topicGenetic Neurodegenerative DiseasesFrench-language works237,207