Threaded versus porous-surfaced implants as anchorage units for orthodontic treatment: three-dimensional finite element analysis of peri-implant bone tissue stresses.
Bibliographic record
Abstract
PURPOSE: A 3-dimensional finite element model was developed to investigate the cause of different crestal bone loss patterns observed around sintered porous-surfaced and machined (turned) threaded dental implants used for orthodontic anchorage in a previously reported animal study. MATERIALS AND METHODS: Twenty-noded structural solid elements with parabolic interpolation between nodes were used for modeling the bone-implant interface zone. A 3-N traction force acting between either 2 porous-surfaced or 2 machined threaded implants placed in canine premolar mandibular sites and bone profiles observed at initiation and 22 weeks of orthodontic loading were modeled. RESULTS: Higher maximum stresses in peri-implant bone next to the coronal region of the implants were predicted with the machined threaded implants at both the initial and final time points, with the values 20% greater than those predicted after the 22-week loading period. These values were approximately 200% greater than those predicted for the porous-surfaced implants, for which a more uniform stress distribution was predicted. DISCUSSION: The finite element model results indicated that the observed greater retention of crestal bone next to the porous-surfaced implants was attributable to lower peak stresses developing in crestal peri-implant bone with this design, which decreased the probability of bone loss related to local overstressing and bone microfracture. CONCLUSION: The predicted lower stresses were a result of the more uniform transfer of force from implant to bone with the porous-surfaced implants, which was a consequence of the interlocking of bone and implant possible with this design.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".