Immediate Functional Loading of Single‐Tooth TIO<sub>2</sub> Grit‐Blasted Implant Restorations: A Controlled Prospective Study in a Porcine Model. Part I: Clinical Outcome
Bibliographic record
Abstract
BACKGROUND: Although favorable integration occurs with immediately loaded implants, the relationship between implant outcome, levels of occlusion, and diet requires optimization. PURPOSE: Pertubating load on single implant restorations immediately after placement by a hard food diet will increase the strains at the bone-implant interface, increasing the risk for failure. MATERIALS AND METHODS: Forty-eight implants replaced the first and third mandibular premolars in 12 pigs, allocated into two groups based on soft- and hard-diet feeding. Cylindrical and tapered implants replaced the first and third premolars, respectively. Each animal received at random four different masticatory loading conditions (group 1 control]: implant with either a cover screw or a healing abutment, and group 2 test]: implant with a crown either with or without occlusal contacts). RESULTS: Thirteen implants out of 44 failed in 11 animals (one with a cover screw, one with a healing abutment, three with nonocclusal, and eight with occlusal restorations). The failure rate of restored implants (either in occlusion or not) was significantly higher in the third premolar sites (p=.007), although diet had no significant effect (p=.421). CONCLUSIONS: While diet had no effect on the failure pattern of immediately loaded single implants, the position and type of load under the masticatory mode were significant. Immediately loaded implants both in and out of occlusion were less successful than the controls, and this is probably attributed to detrimental strain induced on the bone-implant interface.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".