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Primary Ciliary Dyskinesia: First Health-related Quality-of-Life Measures for Pediatric Patients

2016· article· en· W2479450187 on OpenAlexafffund
Sharon Dell, Margaret W. Leigh, Jane S. Lucas, Thomas W. Ferkol, Michael R. Knowles, Adrianne Alpern, Laura Behan, Anjana M. Morris, Claire Hogg, Audrey DunnGalvin, Alexandra L. Quittner

Bibliographic record

VenueAnnals of the American Thoracic Society · 2016
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsInstitute for Clinical Evaluative SciencesSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersNational Center for Advancing Translational SciencesNational Institutes of HealthEuropean CommissionHospital for Sick ChildrenWellcome TrustNational Heart, Lung, and Blood InstituteGilead SciencesAAIR CharityNational Institute for Health and Care Research
KeywordsMedicinePrimary ciliary dyskinesiaFocus groupQuality of life (healthcare)Family medicinePopulationEthnic groupQualitative researchDiseaseClinical psychologyPediatricsPathologyNursing

Abstract

fetched live from OpenAlex

Abstract Rationale Primary ciliary dyskinesia (PCD) is a rare disease. There are no available data on disease-specific pediatric patient–reported outcomes. Objectives Our objective was to create developmentally appropriate, health-related quality-of-life questionnaires (QOL-PCD) for children (6–12 yr) and adolescents (13–17 yr) with PCD and a parent proxy measure. Methods The QOL-PCD was developed using a cross-cultural protocol-driven approach satisfying both North American and European drug regulatory agency guidelines. A conceptual framework was generated by literature review, focus groups (expert clinicians and patients/parents), and open-ended interviews with children, adolescents, and parents of patients with PCD. We recruited participants from international research consortiums, PCD clinics, and patient advocacy groups, aiming for representation of a wide spectrum of disease severity, sociodemographic status, and ethnicity. Qualitative interviews were conducted by trained and experienced research assistants and psychologists. Transcripts were content-analyzed with Atlas.ti/NVivo to assess saturation of content. A self-completed item relevance survey was administered to E.U. participants. Qualitative and quantitative data were used to construct draft instruments. Questionnaires were further refined after cognitive interviews. Measurements and Main Results: Focus groups (n = 62 experts; n = 20 patients/parents) and open-ended interviews with patients/parents (n = 69; 34 males; age at diagnosis, 0–15 yr; FEV1, 58–118% predicted) revealed a wide spectrum of issues unique to this population. Content analysis of transcripts identified the following domains, depending on age: Respiratory Symptoms, Physical Functioning, Emotional Functioning, Treatment Burden, Ears and Hearing, Sinus Symptoms, Social Functioning, Role Functioning, Vitality, Health Perceptions, School Functioning, and Eating and Weight. Various items were retained in questionnaires, based on age and role of respondent: 37, 43, and 41 items for children, adolescents, and parent proxy, respectively. The item relevance survey (n = 57) yielded results similar to those of open-ended interviews. Cognitive testing (n = 47; 20 males; age at diagnosis, 0–11 yr; FEV1, 49–124% predicted) confirmed that items and response choices were clear and understood by respondents, and that all relevant items were included. Conclusions The QOL-PCD measures, developed using rigorous, protocol-driven methods and international collaborations, have demonstrated content validity and cross-cultural equivalence for implementation in English-speaking populations. Psychometric testing is underway to determine their measurement properties for evaluating clinical interventions and informing quality of care.

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)0.005
metaresearch head score (Gemma)0.009
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 score0.005
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.120
GPT teacher head0.428
Teacher spread0.307 · 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".

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Citations50
Published2016
Admission routes2
Has abstractyes

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