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Record W2905720564 · doi:10.7224/1537-2073.2017-109

Perspectives of Patients with Multiple Sclerosis on Drug Treatment

2018· article· en· W2905720564 on OpenAlexaboutno aff
Larry D. Lynd, Natalie Henrich, Celestin Hategeka, Carlo A. Marra, Nicole Mittmann, Charity Evans, Anthony Traboulsee

Bibliographic record

VenueInternational Journal of MS Care · 2018
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMultiple sclerosisDiseaseDrugDrug treatmentIntensive care medicineFamily medicinePsychiatryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Patients experience multiple sclerosis (MS) differently based on their disease type and other factors. This study aimed to explore the relative importance that patients with MS place on various attributes of MS drug therapies and to elucidate these patients' preferences regarding treatment characteristics such as administration, potential benefits, and side effects of the therapies. METHODS: Focus groups were conducted in Vancouver, Canada, with 23 adult patients with MS. Participants were interviewed in three groups based on disease category and MS treatment experience: treatment-naive, non-treatment-naive relapsing-remitting and non-treatment-naive progressive MS. RESULTS: Overall, the most important characteristics of MS drugs were effectiveness and side effects. As such, there is hesitancy about trying new-to-market drugs because the risks, benefits, and costs may not be well known. Participants valued stability in their treatment and generally did not want to take on the additional risk of trying a new drug if they felt that their current medication was providing benefit. Convenience and method of administration were secondary considerations that would generally be valued only if expected risks and benefits were considered equal or superior. CONCLUSIONS: This qualitative study shows that patients consider the impact and likelihood of benefits and side effects first and foremost when making drug treatment decisions and that other factors, such as convenience and method of administration, are of secondary concern.

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)0.000
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 score0.032
Threshold uncertainty score0.262

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.037
GPT teacher head0.317
Teacher spread0.280 · 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

Citations20
Published2018
Admission routes1
Has abstractyes

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