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Record W2613579314 · doi:10.1080/09546634.2017.1329499

What is clearance worth? Patients’ stated risk tolerance for psoriasis treatments

2017· article· en· W2613579314 on OpenAlexaff
Angelyn Fairchild, Shelby D. Reed, F. Reed Johnson, Greg Anglin, Anne M. Wolka, Rebecca Noel

Bibliographic record

VenueJournal of Dermatological Treatment · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPsoriasis: Treatment and Pathogenesis
Canadian institutionsEli Lilly (Canada)
FundersDuke UniversityEli Lilly and Company
KeywordsMedicinePsoriasisPlaque psoriasisRegimenDermatologyLogistic regressionSurgeryInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this study was to provide quantitative evidence of patients' tolerance for therapeutic risks associated with psoriasis treatments that could offer psoriasis improvements beyond the PASI 75 benchmark. MATERIALS AND METHODS: We used a discrete-choice experiment in which respondents chose between competing psoriasis treatments characterized by benefits (i.e. reduced plaque severity, reduced plaque area), risks (i.e. 10-year risk of tuberculosis, 10-year risk of death from infection), and treatment regimen. We analyzed choice data using random-parameters logit models for psoriasis affecting the body, face, or hands. RESULTS: Of 927 eligible members of the National Psoriasis Foundation who completed the survey, 28% were unwilling to accept any greater risk of treatment-related infection mortality. Among the remaining 72%, respondents were willing to accept higher risks of infection-related mortality associated with treatment to completely remove plaques covering only 1% of the body, compared to reducing lesions from 10 to 1% of the affected area. This finding was more pronounced for lesions on the face. CONCLUSIONS: Most patients placed greater value on eliminating even very small plaques compared to avoiding treatment-related risks. The perceived importance of complete versus near-complete clearance was stronger than previously documented.

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 categoriesMeta-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 score0.469
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.032
GPT teacher head0.286
Teacher spread0.254 · 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

Citations19
Published2017
Admission routes1
Has abstractyes

Explore more

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