Dental health state utility values associated with tooth loss in two contrasting cultures
Bibliographic record
Abstract
The study aimed to assess the value placed on oral health states by measuring the utility of mouths in which teeth had been lost and to explore variations in utility values within and between two contrasting cultures, UK and Iran. One hundred and fifty eight patients, 84 from UK and 74 from Iran, were recruited from clinics at University-based faculties of dentistry. All had experienced tooth loss and had restored or unrestored dental spaces. They were presented with 19 different scenarios of mouths with missing teeth. Fourteen involved the loss of one tooth and five involved shortened dental arches (SDAs) with varying numbers of missing posterior teeth. Each written description was accompanied by a verbal explanation and digital pictures of mouth models. Participants were asked to indicate on a standardized Visual Analogue Scale how they would value the health of their mouth if they had lost the tooth/teeth described and the resulting space was left unrestored. With a utility value of 0.0 representing the worst possible health state for a mouth and 1.0 representing the best, the mouth with the upper central incisor missing attracted the lowest utility value in both samples (UK = 0.16; Iran = 0.06), while the one with a missing upper second molar the highest utility values (0.42, 0.39 respectively). In both countries the utility value increased as the tooth in the scenario moved from the anterior towards the posterior aspect of the mouth. There were significant differences in utility values between UK and Iranian samples for four scenarios all involving the loss of anterior teeth. These differences remained after controlling for gender, age and the state of the dentition. With respect to the SDA scenarios, a mouth with a SDA with only the second molar teeth missing in all quadrants attracted the highest utility values, while a mouth with an extreme SDA with both missing molar and premolar teeth in all quadrants attracted the lowest utility values. The study provided further evidence of the validity of the scaling approach to utility measurement in mouths with missing teeth. Some cross-cultural variations in values were observed but these should be viewed with due caution because the magnitude of the differences was small.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".