Mechanical Properties of Three-Dimensional Printed Provisional Resin Materials for Crown and Fixed Dental Prosthesis: A Systematic Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The emergence of digital dentistry has led to the introduction of various three-dimensional (3D) printing materials in the market, specifically for provisional fixed restoration. This study aimed to undertake a systematic review of the published literature on the Mechanical Properties of 3D- Printed Provisional Resin Materials for crown and fixed dental prosthesis (FDP). The electronic database on PubMed/Medline was searched for relevant studies. The search retrieved articles that were published from January 2011 to March 2023. The established focus question was: "Do provisional 3D-printed materials have better mechanical properties than conventional or milled provisional materials?". The systematically extracted data included the researcher's name(s), publication year, evaluation method, number of samples, types of materials, and study outcome. A total of 19 studies were included in this systematic review. These studies examined different aspects of the mechanical properties of 3D-printed provisional materials. Flexural Strength and Microhardness were the frequently used mechanical testing. Furthermore, 3D-printed provisional restorations showed higher hardness, smoother surfaces, less wear volume loss, and higher wear resistance compared to either milled or conventional, or both. 3D-printed provisional resin materials appear to be a promising option for fabricating provisional crowns and FDPs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 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 it