Validation of clinically related aging models based on enamel wear
Bibliographic record
Abstract
PURPOSE: Physiological and erosive wear reported in clinical studies were reviewed, and in vitro aging models were developed to simulate and compare the effect of aging on human teeth with the review data obtained from clinical studies. METHODS: A review of clinical studies and randomized clinical trials that quantify enamel wear was performed in the PubMed database. The first in vitro analysis evaluated the effect of mechanical chewing simulation only. Enamel specimens were aged in the chewing simulator (up to 1.2 million cycles) with two occlusal loads (30 and 50 N). In the second in vitro analysis, specimens were aged in two aging models. The first model (MT) simulated mechanical and thermal oral challenges: MT1- 240,000 chewing and 10,000 thermal cycles, MT2- 480,000 chewing and 20,000 thermal cycles, MT3- 1.2 million chewing and 50,000 thermal cycles. The second model (MTA) simulated mechanical, thermal, and acidic oral challenges as follows: MTA1- 240,000 chewing, 10,000 thermal and 3-h acidic cycles; MTA2: 480,000 chewing, 20,000 thermal and 6-h acidic cycles, MTA3- 1.2 million chewing, 50,000 thermal and 15-h acidic cycles. RESULTS: The review included 13 clinical studies evaluating tooth wear (eight physiological and five erosive). The results estimated the annual average physiological wear as 38.4 µm (9.37-51). In comparison, the MT1 showed wear of 60 (24) µm. Also, the average annual erosive wear in the literature was 179.5 µm (70-265) compared to MTA1-induced wear of 209 (14) µm. CONCLUSION: There was wide variation in tooth wear reported in clinical studies, suggesting a critical need for more accurate studies, possibly based on scanning technologies. Despite this, the data reported using the novel aging models are within a range to be considered consistent with and to simulate tooth wear measured in vivo.
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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 | 0.034 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".