Estimating the weight of dental amalgam restorations.
Bibliographic record
Abstract
AIM: Data on the weights of amalgam restorations are lacking. The aim of this study was to determine these weights and to develop criteria to facilitate their estimation. METHODS: Four separate regression models with 4 covariates in various combinations were used to estimate the weight of amalgam restorations. Model I, based on 514 restorations from both natural and anatomical replica teeth, contained 3 covariates: the number of restored surfaces (covariate A), the type of tooth (covariate B) and whether the restoration had been removed from a natural tooth or an anatomical replica tooth (covariate C). Model II, based on 359 restorations from anatomical replicas, contained 2 covariates: A and B. Model III, based on 155 restorations from natural teeth, contained 3 covariates: covariates A and B and whether the natural teeth had been extracted in 2002 or at least 15 years previously (covariate D). In model IV, covariate D was removed from model III. RESULTS: Model I explained 72% of the variation in the weight of restorations; the partial R2 for covariates A, B and C in model I was 0.5818, 0.797 and 0.0579, respectively (p < 0.001). In model III, the weights of the restorations did not depend on covariate D (p = 0.93). The least square mean weight of amalgam restorations with 1, 2, 3, and 4 or more surfaces restored (and 95% confidence interval) was 0.31 g (0.28-0.34 g), 0.49 g (0.45-0.53 g), 0.81 g (0.76-0.86 g) and 1.38 g (1.31-1.45 g), respectively. CONCLUSION: The number of surfaces restored (covariate A) accounted for at least 80% of the variation in the weight of restorations in all models and therefore provides the best estimate for the weight of amalgam restorations.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".