Evaluation of Retentive Strength of 50 Endodontically-Treated Single-Rooted Mandibular Second Premolars Restored with Cast Post Cores Using 5 Common Luting (Cement) Agents
Bibliographic record
Abstract
BACKGROUND A luting agent is a dental cement used to secure a dental restoration. This study aimed to evaluate retentive strength of 50 endodontically-treated single-rooted mandibular second premolars (extracted) restored using 5 common luting (cement) agents. MATERIAL AND METHODS Fifty single-rooted mandibular second premolars with adequate root length and uniform size/shape were decoronated. After completing endodontic biomechanical preparation and obturation, root canals of all specimens were prepared to receive a cast post core. Depending upon cementation type, CPC specimens were divided in 5 groups (10 each) (Gp): Zinc phosphate (Gp ZP), polycarboxylate (Gp PC), glass ionomer (Gp GI), resin-modified glass ionomer (Gp RGI), and resin cement (Gp RC). Retentive strength was determined using the adhesive failure pull-out test. Mean/standard deviations were calculated for tensile forces (in kilograms) and differences were determined using analysis of variance (ANOVA). Multiple comparison was performed using the t test. A P value of ≤0.05 indicated a statistically significant difference. RESULTS The order of mean tensile strength from higher to lower was Gp RC (21.46) >Gp RGI (18.17) >Gp GI (16.07) >Gp ZP (15.33) >Gp PC (13.63). Differences in retentive strengths between the cements were significant (P≤0.05). Multiple-group comparisons showed that except for Gp ZP and Gp GI, all groups differed significantly from each other. CONCLUSIONS All investigated cements provided optimal retentive strengths, with wide differences between them. Resin cements should be used when CPC removal is not anticipated, while polycarboxylate or zinc phosphate should be used if CPC removal is anticipated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".