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Record W4388191285 · doi:10.2147/ccid.s429120

Development and Validation of a Patient-Reported Outcome Measure for Fingernail and Toenail Conditions: The NAIL-Q

2023· article· en· W4388191285 on OpenAlexafffund
Anne F. Klassen, Charlene Rae, Maureen O’Malley, Trisia Breitkopf, Leah Algu, Jasmine Mansouri, Claire Brown, Yì Wáng, Shari R. Lipner

Bibliographic record

VenueClinical Cosmetic and Investigational Dermatology · 2023
Typearticle
Languageen
FieldMedicine
TopicNail Diseases and Treatments
Canadian institutionsImpactWestern UniversityMcMaster University
FundersMcMaster University
KeywordsCronbach's alphaRasch modelPatient-reported outcomeIntraclass correlationDebriefingCognitive interviewNail (fastener)Construct validityReliability (semiconductor)PromConvergent validityPsychologyPsychometricsContent validityMedicineClinical psychologyQuality of life (healthcare)CognitionSocial psychologyPsychiatryDevelopmental psychologyInternal consistencyNursing

Abstract

fetched live from OpenAlex

Background: Patient-reported outcome measures (PROMs) are needed to measure outcomes that matter to people with nail conditions, from their perspective. Objective: To design a comprehensive new PROM (NAIL-Q) to measure outcomes important in toenail and fingernail conditions. Methods: A mixed methods iterative approach was used. Phase 1 involved concept elicitation interviews that were audio-recorded, transcribed, and coded line-by-line. Concepts were developed into scales and refined through cognitive debriefing interviews with patients and expert input. Data was then collected from an international sample using a crowdsource platform. Eligible participants were aged ≥18 years with a nail condition for at least 3 months. Rasch Measurement Theory (RMT) analysis was used to examine item and scale performance. Other psychometric tests included test-retest reliability, and convergent and construct validity. Results: Phase 1 interviews involved 23 patients with 10 nail conditions and input from 11 dermatologists. The analysis led to the development of 84 items for field-testing. In Phase 2, 555 participants completed the survey. Toenail conditions (n = 441) were more common than fingernail conditions (n = 186). The RMT analysis reduced the number of items tested to 45 in 7 scales measuring nail appearance, health-related quality of life concerns, and treatment outcomes. All items had ordered thresholds and nonsignificant chi-square p values. Reliability statistics with and without extremes for the Person Separation Index were ≥0.79 and Cronbach's alpha were ≥0.83, and for intraclass correlation coefficients were ≥0.81. Construct validity was further supported in that most participants agreed that the NAIL-Q was easy to understand, asked relevant and important questions in a respectful way, and that it should be used to inform clinical care. Conclusion: The NAIL-Q is a rigorously designed and tested PROM that measures nail appearance, health-related quality of life and treatment outcomes. This PROM can be used in clinical practice to inform patient care and to include the patient perspective in research.

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)0.047
metaresearch head score (Gemma)0.061
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.249

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0470.061
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.146
GPT teacher head0.388
Teacher spread0.242 · 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
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

Citations6
Published2023
Admission routes2
Has abstractyes

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