Dental Discomfort Questionnaire: its use with children with a learning disability
Bibliographic record
Abstract
This study investigated whether the behaviors from the Dental Discomfort Questionnaire (DDQ) could help identify toothaches in children with a learning disability, who have a limited capacity to self-report. The objectives were to examine whether the behaviors from the DDQ occur more often in children with a learning disability who have caries and a toothache than in children who do not have caries and a toothache; and secondly, to examine whether two additional items increase the specificity and sensitivity of the DDQ to recognize a toothache, in this particular population of children with a learning disability. The DDQ was completed by a convenience sample of 58 parents on behalf of their children: 31% girls, aged between 6 and 13 years (mean = 7.5, SD = 2.7). Of the total group, 26% (n = 15) suffered from a toothache and 43% (n = 25) had carious teeth. Children with caries and a toothache had a significantly higher mean DDQ score and displayed more toothache-related behaviors (e.g., problems with chewing, problems with brushing teeth) than children without caries or toothache. The DDQ seems to be a functional and easy-to-use instrument to alert parents to the presence of a toothache in this specific group of children with a learning disability.
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 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".