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Validation of the Brazilian versions of two inventories for measuring oral health‐related quality of life of edentulous subjects

2010· article· en· W1503532918 on OpenAlexaff
Raphael Freitas de Souza, Andrea Sayuri Silveira Dias Terada, Maria Paula Della Vecchia, Rômulo Rocha Regis, Ana Paula Zanini, Marco Antônio Compagnoni

Bibliographic record

VenueGerodontology · 2010
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineQuality of life (healthcare)Oral healthConstruct validityCorrelationRehabilitationGerontologyRegression analysisAnalysis of varianceConcurrent validityDentistryClinical psychologyPsychometricsPhysical therapyStatistics

Abstract

fetched live from OpenAlex

OBJECTIVES: To analyse the validity of the Brazilian versions of OHIP-EDENT and GOHAI as assessment tools of edentulous subjects' OHRQoL. BACKGROUND: Inventories for measuring oral health-related quality of life (OHRQoL) are important in clinical studies regarding oral rehabilitation. However, there is a need for comprehensive validation after translation into different cultural settings. MATERIALS AND METHODS: The sample comprised of 100 complete denture wearers (29 men, 71 women, mean age of 65.2 ± 9.9 years). The associations between each OHRQoL inventory and other variables served as measurements of construct validity. Data analysis comprised the Spearman correlation test as well as multiple regression using the OHRQoL inventories as dependent variables and the other scales as determinants. RESULTS: Both OHRQoL inventories showed good correlation with denture satisfaction, whereas lower correlation coefficients were found among the inventories and the HAD subscales. Denture satisfaction alone explained 48% and 39% of the variance found for the OHIP-EDENT and GOHAI, respectively, as assessed by multiple regression. A smaller effect was found for OHIP-EDENT. CONCLUSION: Both OHIP-EDENT and GOHAI showed good construct validity for measurement of OHRQoL of edentulous subjects.

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)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.082
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.074
GPT teacher head0.368
Teacher spread0.294 · 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 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

Citations52
Published2010
Admission routes1
Has abstractyes

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