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Record W1993621631 · doi:10.2310/7750.2014.14017

PCQoL

2015· article· en· W1993621631 on OpenAlexaff
Mariam Abbas, Mary Schwartz, Frances J.D. Smith, W.H. Irwin McLean, Peter Hull

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSkin and Cellular Biology Research
Canadian institutionsUniversity of SaskatchewanUniversity of Alberta
Fundersnot available
KeywordsRasch modelMedicineConstruct validityQuality of life (healthcare)Dermatology Life Quality IndexReliability (semiconductor)Classical test theoryInternal consistencyPsychometricsDiseaseClinical psychologyItem response theoryStatisticsPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Pachyonychia congenita (PC) is a rare but often debilitating, dominantly inherited disorder. New treatments require more accurate instruments for evaluating changes in the quality of life in these patients. OBJECTIVES: This study was undertaken to develop and validate a quality of life questionnaire for PC patients (PCQoL). METHODS: Relevant factors influencing quality of life in PC patients were identified and incorporated into the well-recognized, general questionnaire, the Dermatology Life Quality Index (DLQI), to establish a disease-specific measure, the PCQoL. Classical test theory (CTT) and Rasch analysis (RA) were used to analyze and validate the PCQoL. RESULTS: CTT analysis established test-retest reliability and internal consistency for the PCQoL. Concurrent and construct validity for the DLQI and the PCQoL were also validated. Chi-square-based infit and outfit statistics indicated that the Rasch model fits the observed responses very well. RA reconfirmed reliability, internal consistency, reasonable homogeneity, construct validity, and the presence of three RA-based domains. CONCLUSION: The PCQoL questionnaire is a measure validated by both CTT and RA. It appears to be a valuable tool in measuring quality of life modifications in PC individuals with keratoderma.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.252
Threshold uncertainty score0.194

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.044
GPT teacher head0.301
Teacher spread0.257 · 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 designNot applicable
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

Citations7
Published2015
Admission routes1
Has abstractyes

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