The oral assessment in Down syndrome questionnaire (OADS): development of an instrument to evaluate oral health problems in individuals with Down syndrome.
Bibliographic record
Abstract
OBJECTIVE: To develop a French language instrument whose primary aim is the description of oral health status in Down syndrome (DS) individuals. METHOD: Due to the reduced intellectual capacity in DS individuals, the instrument was designed to be completed by their parents. Items were generated through a literature review plus interviews with relevant professionals and DS parents. Following pilot testing, the 31-item questionnaire was divided into seven domains (access, function, development, signs, pain, disability and a global evaluation of oral health status), and subjected to a psychometric evaluation of internal reliability, test-retest reliability and discriminant validity. Internal reliability was assessed through evaluation of Cronbach's alpha or the Kuder-Richardson-20 value where appropriate. Test-retest reliability was assessed through the evaluation of the intra-class correlation coefficient (ICC) for each domain. Discriminant validity was assessed through evaluations of hypothesised differences in domain scores between different groups of DS individuals by age and between DS and non-DS individuals. The theoretical domain categorisation was empirically evaluated through the generation of inter-item correlation coefficients. RESULTS: Internal reliability coefficients ranged from 0.43 for the disability domain to 0.83 for the function domain, while the domain ICCs ranged from 0.56 to 0.77 for the signs and function domains respectively. For discriminant validity, the domains largely performed as hypothesised. Finally, inter-item correlation coefficients largely supported the proposed domain structure of the questionnaire. CONCLUSION: This investigation of the psychometric properties of a proxy. French language, assessment of oral health problems in the DS population has demonstrated an instrument with good preliminary indicators of reliability and validity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".