Selective carious tissue removal and glass ionomer liner reduction of pulp stress in bulk fill resin composite restorations
Bibliographic record
Abstract
To evaluate the effect of selective or nonselective carious tissue removal and the use of a resin-modified glass ionomer (RMGIC) liner under bulk fill resin composite restoration on the stress at the pulp chamber, the elastic moduli of hard, firm, soft and intact dentin were calculated using nanoindentation. Post-gel shrinkage of the bulk fill resin composite and RMGIC were determined using the strain-gauge method. Six finite element models were created by using digital radiography with the combination of two study factors: a) carious tissue removal: selective removal or nonselective removal of carious tissue, and b) use of RMGIC liner: with or without 1.0 mm of RMGIC liner. The modified von Mises stresses (mvm) (MPa) were extracted on the nodes of the internal wall of the pulp ceiling chamber at 100 N occlusal loading. Data were analyzed descriptively and recorded quantitively. Both study factors influenced the stress distribution. The mvm stress during the restorative procedure was higher for nonselective carious tissue removal without RMGIC (25.9 MPa) and lower for selective carious tissue removal associated with RMGIC (13.5 MPa). The dentin elastic modulus increased from soft carious (3.6 ± 0.3 MPa) to firm carious (5.2 ± 1.0 MPa) to hard carious (10.9 ± 1.2 MPa) to intact dentin (22.7 ± 3.0 MPa). Molars with carious lesions showed high mvm stress at the pulp ceiling (89.6 MPa) and at fragilized coronal structure remaining. Selective carious tissue removal followed by restoration using a Vitrebond liner and Tetric N-Ceram Bulk fill reduced the stress at the pulp chamber ceiling.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".