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A study protocol for quantifying patient preferences in neuromuscular disorders: a case study of the IMI PREFER Project

2020· preprint· en· W3093682693 on OpenAlexaff
Cecilia Jimenez‐Moreno, Cathy Anne Pinto, Bennett Levitan, Chiara Whichello, Christine Dyer, Eline van Overbeeke, Esther W. de Bekker‐Grob, Ian Smith, Isabelle Huys, Jennifer Viberg Johansson, Kate Adcock, Kristin M. Bullock, Vikas Soekhai, Zhong Yu Yuan, Hanns Lochmüller, G. Ardine de Wit, Gráinne S. Gorman

Bibliographic record

VenueWellcome Open Research · 2020
Typepreprint
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsUniversity of Ottawa
FundersMaryland Sea Grant, University of MarylandInnovative Medicines InitiativeEuropean CommissionNewcastle UniversityWellcomeEuropean Federation of Pharmaceutical Industries and AssociationsMuscular Dystrophy UKLily FoundationUnited Mitochondrial Disease FoundationWellcome Trust
KeywordsProtocol (science)PreferenceMedicineProduct (mathematics)Alternative medicinePathology

Abstract

fetched live from OpenAlex

Objectives: Patient preference studies are increasingly used to inform decision-making during the medical product lifecycle but are rarely used to inform early stages of drug development. The primary aim of this study is to quantify treatment preferences of patients with neuromuscular disorders, which represent serious and debilitating conditions with limited or no treatment options available. Methods: This quantitative patient preferences study was designed as an online survey, with a cross-over design. This study will target two different diseases from the neuromuscular disorders disease group, myotonic dystrophy type 1 (DM1) and mitochondrial myopathies (MM). Despite having different physio-pathological pathways both DM1 and MM manifest in a clinically similar manner and may benefit from similar treatment options. The sample will be stratified into three subgroups: two patient groups differentiated by age of symptom onset and one caregivers group. Each subgroup will be randomly assigned to complete two of three different preference elicitation methods at two different time points: Q-methodology survey, discrete choice experiment, and best-worst scaling type 2, allowing cross-comparisons of the results across each study time within participants and within elicitation methods. Additional variables such as sociodemographic, clinical and health literacy will be collected to enable analysis of potential heterogeneity. Ethics and Dissemination: This study protocol has undergone ethical review and approval by the Newcastle University R&D Ethics Committee (Ref: 15169/2018). All participants will be invited to give electronic informed consent to take part in the study prior accessing the online survey. All electronic data will be anonymised prior analysis. This study is part of the Patient Preferences in Benefit-Risk Assessments during the Drug Life Cycle (IMI-PREFER) project, a public-private collaborative research project aiming to develop expert and evidence-based recommendations on how and when patient preferences can be assessed and used to inform medical product decision making.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.120
metaresearch head score (Gemma)0.095
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.120
Threshold uncertainty score0.632

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1200.095
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0050.003
Scholarly communication0.0030.003
Open science0.0040.004
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0580.015

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 head0.776
GPT teacher head0.621
Teacher spread0.155 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

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

Citations9
Published2020
Admission routes1
Has abstractyes

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