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Record W4288855303 · doi:10.1111/bjd.21798

Factors associated with treatment satisfaction in patients with hidradenitis suppurativa: results from the Global VOICE project

2022· article· en· W4288855303 on OpenAlexaff
Bria Midgette, Andrew Strunk, Oleg E. Akilov, Afsáneh Alavi, Christine B. Ardon, Falk G. Bechara, Arnon D. Cohen, Steven A. Cohen, Steven Daveluy, V. del Mármol, M. Delage, Solveig Esmann, Shani Fisher, Evangelos J. Giamarellos‐Bourboulis, Amelia Głowaczewska, Noah Goldfarb, Elena Gonzalez Brant, Øystein Grimstad, Sandra Guilbault, Iltefat Hamzavi, Rosalind Hughes, John R Ingram, Gregor B. E. Jemec, Qiang Ju, Naomi Kappe, Brian Kirby, Joslyn S. Kirby, Michelle A. Lowes, Łukasz Matusiak, Stella Micha, Robert G. Micheletti, Angela P. Miller, Dagfinn Moseng, Haley B. Naik, Aude Nassif, Georgios Nikolakis, So Yeon Paek, J.C. Pascual, Errol P. Prens, Barry I. Resnik, Hassan Riad, Christopher J. Sayed, Saxon D. Smith, Yssra Soliman, Jacek C. Szepietowski, Jerry Tan, Linnea Thorlacius, Thrasyvoulos Tzellos, Hessel H. van der Zee, Bente Villumsen, Lanqi Wang, Christos C. Zouboulis, Amit Garg

Bibliographic record

VenueBritish Journal of Dermatology · 2022
Typearticle
Languageen
FieldMedicine
TopicHidradenitis Suppurativa and Treatments
Canadian institutionsUniversity of WindsorWestern University
FundersEli Lilly and Company
KeywordsHidradenitis suppurativaMedicinePatient satisfactionMEDLINEDermatologyInternal medicineSurgeryBiologyDisease

Abstract

fetched live from OpenAlex

BACKGROUND: Nearly half of patients with hidradenitis suppurativa (HS) report dissatisfaction with their treatment. However, factors related to treatment satisfaction have not been explored. OBJECTIVES: To measure associations between treatment satisfaction and clinical and treatment-related characteristics among patients with HS. METHODS: Treatment satisfaction was evaluated utilizing data from a cross-sectional global survey of patients with HS recruited from 27 institutions, mainly HS referral centres, in 14 different countries from October 2017 to July 2018. The primary outcome was patients' self-reported overall satisfaction with their current treatments for HS, rated on a five-point scale from 'very dissatisfied' to 'very satisfied'. RESULTS: The final analysis cohort comprised 1418 patients with HS, most of whom were European (55%, 780 of 1418) or North American (38%, 542 of 1418), and female (85%, 1210 of 1418). Overall, 45% (640 of 1418) of participants were either dissatisfied or very dissatisfied with their current medical treatment. In adjusted analysis, patients primarily treated by a dermatologist for HS had 1·99 [95% confidence interval (CI) 1·62-2·44, P < 0·001] times the odds of being satisfied with current treatment than participants not primarily treated by a dermatologist. Treatment with biologics was associated with higher satisfaction [odds ratio (OR) 2·36, 95% CI 1·74-3·19, P < 0·001] relative to treatment with nonbiologic systemic medications. Factors associated with lower treatment satisfaction included smoking (OR 0·78, 95% CI 0·62-0·99; active vs. never), depression (OR 0·69, 95% CI 0·54-0·87), increasing number of comorbidities (OR 0·88 per comorbidity, 95% CI 0·81-0·96) and increasing flare frequency. CONCLUSIONS: There are several factors that appear to positively influence satisfaction with treatment among patients with HS, including treatment by a dermatologist and treatment with a biologic medication. Factors that appear to lower treatment satisfaction include active smoking, depression, accumulation of comorbid conditions and increasing flare frequency. Awareness of these factors may support partnered decision making with the goal of improving treatment outcomes. What is already known about this topic? Nearly half of patients with hidradenitis suppurativa report dissatisfaction with their treatments. What does this study add? Satisfaction with treatment is increased by receiving care from a dermatologist and treatment with biologics. Satisfaction with treatment is decreased by tobacco smoking, accumulation of comorbid conditions including depression, and higher flare frequency. What are the clinical implications of this work? Awareness of the identified factors associated with poor treatment satisfaction may support partnered decision making and improve treatment outcomes.

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 score0.086
Threshold uncertainty score0.568

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.024
GPT teacher head0.255
Teacher spread0.230 · 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

Citations28
Published2022
Admission routes1
Has abstractyes

Explore more

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