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Record W4367043720 · doi:10.1016/j.jdin.2023.03.013

The Personalized Acne Treatment Tool — Recommendations to facilitate a patient-centered approach to acne management from the Personalizing Acne: Consensus of Experts

2023· article· en· W4367043720 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJAAD International · 2023
Typearticle
Languageen
FieldMedicine
TopicAcne and Rosacea Treatments and Effects
Canadian institutionsWindsor Clinical ResearchUniversity of WindsorWestern University
FundersBausch HealthSun PharmaLEO PharmaGaldermaSanofiPfizerAllerganAmorepacific CorporationEli Lilly and Company
KeywordsAcneMedicineDelphi methodMEDLINEAcne treatmentPersonalized medicineHealth careDiseaseDisease managementPatient satisfactionDermatologyBioinformaticsComputer sciencePathologySurgery

Abstract

fetched live from OpenAlex

BackgroundAcne, a commonly treated skin disease, requires patient-centered management due to its varying presentations, chronicity, and impact on health-related quality of life. Despite this, evidence-based clinical guidelines focus primarily on clinical severity of facial acne, omitting important patient- and disease-related factors, including ongoing management.ObjectivesTo generate recommendations to support patient-centered acne management, which incorporate priority and prognostic factors beyond conventional clinical severity, traditionally defined by grading the appearance and extent of visible lesions.MethodsThe Personalizing Acne: Consensus of Experts consisted of 17 dermatologists who used a modified Delphi approach to reach consensus on statements regarding patient- and treatment-related factors pertaining to patient-centered acne management. Consensus was defined as ≥75% voting “agree” or “strongly agree.”ResultsRecommendations based on factors such as acne sequelae, location of acne, high burden of disease, and individual patient features were generated and incorporated into the Personalized Acne Treatment Tool.LimitationsRecommendations are based on expert opinion, which may differ from patients’ perspectives. Regional variations in healthcare systems may not be represented.ConclusionsThe Personalizing Acne: Consensus of Experts panel provided practical recommendations to facilitate individualized management of acne, based on patient features, which can be implemented to improve treatment outcomes, adherence, and patient satisfaction. Acne, a commonly treated skin disease, requires patient-centered management due to its varying presentations, chronicity, and impact on health-related quality of life. Despite this, evidence-based clinical guidelines focus primarily on clinical severity of facial acne, omitting important patient- and disease-related factors, including ongoing management. To generate recommendations to support patient-centered acne management, which incorporate priority and prognostic factors beyond conventional clinical severity, traditionally defined by grading the appearance and extent of visible lesions. The Personalizing Acne: Consensus of Experts consisted of 17 dermatologists who used a modified Delphi approach to reach consensus on statements regarding patient- and treatment-related factors pertaining to patient-centered acne management. Consensus was defined as ≥75% voting “agree” or “strongly agree.” Recommendations based on factors such as acne sequelae, location of acne, high burden of disease, and individual patient features were generated and incorporated into the Personalized Acne Treatment Tool. Recommendations are based on expert opinion, which may differ from patients’ perspectives. Regional variations in healthcare systems may not be represented. The Personalizing Acne: Consensus of Experts panel provided practical recommendations to facilitate individualized management of acne, based on patient features, which can be implemented to improve treatment outcomes, adherence, and patient satisfaction.

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.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score0.467

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 head0.090
GPT teacher head0.319
Teacher spread0.228 · 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