Utilities of dentin regeneration among insured and uninsured adults
Bibliographic record
Abstract
OBJECTIVES: This population-based study measured utilities (preferences measured under conditions of uncertainty) of dentin regeneration (DR), a potential new therapy, root canal therapy (RCT), and extraction (EXT). METHODS: A representative sample of dentate adults (aged 18-69 years) was randomly selected from the Detroit area. A computer program was used to administer the standard gamble (SG) method and record utility score (US) for treatment options of a tooth with reversible pulpitis using the SG method. For the SG method, two anchor states were used: filled tooth with full oral health and filled tooth with severe and continuous pain leading to EXT. Additional data were obtained using a self-administered questionnaire. RESULTS: Out of the 807 adults who resided in 446 screened and selected households, a final sample of 630 adults who resided in 368 households were interviewed. The mean US for DR with 75 and 95% success rates were 72.5 and 86.2 (on a 0-100 scale), respectively. The US for RCT and immediate EXT were 75.6 and 31.3, respectively. Eleven per cent of the adults valued DR with 95% success probability higher than a simple filling with full oral health for life. There were no statistically significant differences in the average US of DR between insured and uninsured adults. Factors such as gender, race, education, income and insurance status, experiences with EXTs or root canal treatment, regularity of dental visits, quality of life, and quality of oral health were not significantly associated with the scores of DR. There was, however, a small but significant interaction between race and dental insurance, and race and gender. CONCLUSION: This population-based study found that DR was highly preferred to other standard treatment options.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".