Assessment of the fitness of removable partial denture frameworks manufactured using additive manufacturing/selective laser melting
Bibliographic record
Abstract
The study compared the fitness accuracy of digitally produced removable partial denture frameworks using 3D printing selective laser melting technology. Three groups were fabricated; the first group where the frameworks were produced digitally through digital designing and then the frameworks were printed by selective laser melting additive manufacturing (3DP-G1). The second frameworks groups were produced by the lost wax/casting method (C-G2) and the third group was produced by scanning wax-up of the framework and then printed as in the first group (SP-G3). A total of 6 frameworks were produced from each group. Micro-CT images were used to investigate spaces under the frameworks seated on the master casts at five specified locations. Finally, spaces at the same locations were measured by using light-body polyvinyl siloxane impression materials. There was no significant difference among the spaces calculated underneath the 18 frameworks for the three various groups at a significance level of (α = .05) either at the CT-scan images or by using the silicone registration materials. Removable partial denture frameworks that were produced by 3D printing technology using selective laser melting additive manufacturing have a high level of fitness accuracy comparable to the ones produced by the lost wax/casting method.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".