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Record W2551195902 · doi:10.1177/1203475416677721

Development of a Quality-of-Life Measure for Hidradenitis Suppurativa

2016· article· en· W2551195902 on OpenAlexaff
Mia Sisic, Joslyn S. Kirby, Sanwarjit Boyal, Lisa Plant, Chelsea McLellan, Jerry Tan

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2016
Typearticle
Languageen
FieldMedicine
TopicHidradenitis Suppurativa and Treatments
Canadian institutionsWindsor Clinical ResearchWestern UniversityUniversity of Windsor
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesAgency for Healthcare Research and Quality
KeywordsMedicineHidradenitis suppurativaQuality of life (healthcare)PsychosocialPatient-reported outcomeCognitionCognitive interviewPsychometricsPhysical therapyMeasure (data warehouse)Clinical psychologyGerontologyPsychiatryPathologyNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Hidradenitis suppurativa (HS) is a chronic skin disorder with adverse impacts on both physical and psychosocial well-being. There is presently no validated HS-specific quality-of-life (QoL) measure. OBJECTIVE: The objective of this study is to develop a QoL instrument for HS (HS-QoL) in accordance with recommended standards. METHODS: Patient interviews (concept elicitation) and expert input were used to develop the conceptual framework for outcomes perceived important to patients with HS. A HS-QoL-v1 measure was developed, and cognitive interviews with patients were conducted for pilot testing. RESULTS: Concept elicitation interviews with patients with HS (n = 21) generated 12 themes. Most frequently reported were impacts on daily activities and symptoms due to HS. These themes, along with literature review and input from clinical experts, informed development of the HS-QoL-v1. Nine cognitive interviews were conducted in a pilot test and resulted in the HS-QoL-v2 measure. CONCLUSION: The HS-QoL-v2 is a preliminary QoL instrument for which further psychometric validation and establishment of clinimetric properties will be undertaken.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.679
Threshold uncertainty score0.391

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.097
GPT teacher head0.336
Teacher spread0.239 · 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 designCase report
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

Citations44
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Cutaneous Medicine and SurgerySame topicHidradenitis Suppurativa and TreatmentsFrench-language works237,207