MétaCan
Menu
Back to cohort
Record W2924174256 · doi:10.1111/bjd.17929

Regardless of how risks are framed, patients seem hesitant to use topical steroids for atopic dermatitis

2019· letter· en· W2924174256 on OpenAlexaff
Philip Maghen, Emily Unrue, Elias Oussedik, Abigail Cline, Leah A. Cardwell, Steven R. Feldman

Bibliographic record

VenueBritish Journal of Dermatology · 2019
Typeletter
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsMcGill University
Fundersnot available
KeywordsAtopic dermatitisMedicineDermatology

Abstract

fetched live from OpenAlex

Dear Editor, Topical corticosteroids are highly effective in treating atopic dermatitis (AD); however, patients often fear their potential adverse events.1 This fear may be exacerbated by risk aversion. Risk aversion is the tendency to avoid unknown risks in favour of more certain, but less advantageous outcomes.2 Previous studies suggest that how physicians frame the risks and benefits of a treatment can have an impact on patients’ decision making; if physicians framed actinic keratosis as a precancerous condition instead of a condition that could spontaneously resolve without becoming cancerous, patients preferred treatment.3 Because the word ‘cancer’ may elicit strong emotions, we focused on a lower‐risk dermatological condition to understand the nature of framing better. Physicians often present the benefits alongside the risks of treatment. Risk aversion may lead patients to consider the risk of treatment more heavily than the potential benefits. To assess the effect of ‘framing’, we assessed whether patients with a history of AD were more willing to take a topical corticosteroid treatment when the treatment benefits were rephrased as the risk of not taking treatment. The study was approved by the Wake Forest School of Medicine institutional review board. Informed consent was obtained verbally and assumed based on patient survey completion. A total of 613 participants with a diagnosis of AD and aged 18 years or older were recruited in clinic and divided into three subgroups (Table 1). Within each subgroup, participants were randomized to a script that discussed either the risk of the topical steroid vs. risk of not experiencing eczema improvement or the risk of topical steroid vs. benefit of eczema improvement. Participants used a 10‐point Likert scale (from 1, ‘not willing’ to 10, ‘completely willing’), to rate their willingness to take a treatment described as a topical corticosteroid (Table 1). Scores were treated as ordinal data and evaluated with the Mann–Whitney U‐test. Median willingness to take a treatment based on ‘risk vs. benefit’ and ‘risk vs. risk’ presentation Subgroup 1a: control group (risk vs. benefit); subgroup 1b: intervention group (risk vs. risk). Median willingness to take a treatment based on ‘risk vs. benefit’ and ‘risk vs. risk’ presentation Subgroup 1a: control group (risk vs. benefit); subgroup 1b: intervention group (risk vs. risk). There were no significant differences between the subgroup's baseline characteristics. Participants were 38·4 ± 7·5 years on average, 56% were women and 49% were of white, 12% African American, 4% Hispanic, 28% Asian and 7% were of another ethnicity. Participants reported having eczema for 13·9 ± 4·9 years on average and 99% of participants had attained a high‐school diploma or higher education. Participants in subgroup 1 were more likely to take a treatment when presented with risk of treatment vs. risk of nontreatment (P = 0·041). Participants in subgroups 2 and 3 were as willing to take a treatment for AD whether they were presented with a potential risk of treatment contrasted to its potential benefit, or a potential risk of treatment contrasted to its potential risk of no treatment (Table 1). While the use of risk aversion is well‐studied in behavioural economics, its practice within medicine is nascent.4 Although previous studies suggest that physicians’ framing has an impact on patients’ decision‐making process, our data suggests that framing of risks and benefits was not a predominant factor affecting patients with AD's consideration of treatment.3 Nevertheless, subgroup 1 participants were more willing to take a treatment when presented with risk of treatment vs. risk of nontreatment, which may support the critical importance of word choice and sentence structure in framing. One explanation that may support our findings may be the low‐risk–low‐benefit characteristic of the presented treatment. Since the stakes of risks of both nontreatment and treatment in this study were relatively low, the effect of framing may have been minimal. ‘Order effect’ may also explain our findings. The presentation order of risks and benefits affects participants’ willingness to take a new treatment, especially for low‐risk decisions.5 Another explanation may be that the different wording between the scripts may have affected a participant's view of risk and therefore willingness. Additionally, as different presenters asked survey questions, social cues such as facial expressions or inflection, could have affected participants’ decision making. Physicians should consider asking patients what factors influence their decisions concerning treatment options. While the presentation of ‘risk vs. risk’ or ‘risk vs. benefit’ does not seem to influence the use of a corticosteroid in AD, further exploration of other behavioural economic principles within dermatology may help improve patient adherence and patient outcomes. Funding sources: none. Conflicts of interest: S.R.F. has received research, speaking and/or consulting support from a variety of companies including Galderma, GSK/Stiefel, Almirall, Leo Pharma, Baxter, Boehringer Ingelheim, Mylan, Celgene, Pfizer, Valeant, Taro, Abbvie, Cosmederm, Anacor, Astellas, Janssen, Lilly, Merck, Merz, Novartis, Regeneron, Sanofi, Novan, Parion, Qurient, National Biological Corporation, Caremark, Advance Medical, Sun Pharma, Suncare Research, Informa, UpToDate and National Psoriasis Foundation. He is founder and majority owner of www.DrScore.com and founder and part owner of Causa Research, a company dedicated to enhancing patients’ adherence to treatment.

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)0.003
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.037
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0370.024
Insufficient payload (model declined to judge)0.0090.005

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 head0.257
GPT teacher head0.376
Teacher spread0.118 · 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 designObservational
Domainnot available
GenreCommentary

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

Citations8
Published2019
Admission routes1
Has abstractno

Explore more

Same venueBritish Journal of DermatologySame topicHealth Systems, Economic Evaluations, Quality of LifeFrench-language works237,207