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Record W2746828885 · doi:10.1177/0022034517725707

Quality-of-Life in Children with Orofacial Clefts and Caregiver Well-being

2017· review· en· W2746828885 on OpenAlexfundno aff
Lacey Sischo, Maureen Wilson-Genderson, Hillary L. Broder

Bibliographic record

VenueJournal of Dental Research · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCleft Lip and Palate Research
Canadian institutionsnot available
FundersNational Institute of Dental and Craniofacial ResearchUniversity of Illinois at Urbana-ChampaignYork UniversityChildren's Healthcare of AtlantaNYU Langone Medical CenterNational Institutes of HealthChildren's Hospital of Philadelphia
KeywordsQuality of life (healthcare)MedicineSalientOral healthWell-beingClinical trialPsychologyDentistryComputer sciencePathologyPsychotherapistNursing

Abstract

fetched live from OpenAlex

Quality of life is a valid patient-reported parameter that provides an assessment of treatment need or outcomes complementary to standard clinical measures. Such patient-reported assessments are particularly salient when examining chronic conditions with prolonged treatment trajectories, such as cleft lip and palate. This critical review identifies key questions related to ongoing research on the oral health-related quality of life (OHRQoL) in children with cleft and caregiver well-being. Details of the design and results from 2 longitudinal multicenter studies are presented. This article also provides an update on recent published reports regarding OHRQoL in individuals with cleft. Methodological issues in OHRQoL research are discussed, including condition-specific versus generic instruments, incorporating positive items in OHRQoL instruments, calculating minimally important differences in OHRQoL, implementing mixed methods design, and utilizing validated short assessment forms in OHRQoL research. Finally, new directions for research in cleft as a chronic condition are identified and discussed.

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)0.003
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.883
Threshold uncertainty score0.587

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 science0.0010.000
Research integrity0.0000.001
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 head0.134
GPT teacher head0.476
Teacher spread0.342 · 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
GenreReview

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

Citations47
Published2017
Admission routes1
Has abstractyes

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