Unconventional Implant Placement Part <scp>III</scp>: Implant Placement Encroaching upon Residual Roots – A Report of Six Cases
Bibliographic record
Abstract
BACKGROUND: When a residual root is found in the way of a planned implant placement, invasive surgery is usually performed in order to remove it. Consequently, implant therapy is rendered more complex and lengthy. PURPOSE: We present 6 cases treated according to an unconventional protocol in which invasive surgery was avoided by allowing the implants to encroach upon the residual roots in order to permit a prosthetically driven surgery. MATERIALS AND METHODS: Six patients were treated with 7 implants placed through a residual root (4 in the mandible and 3 in the maxilla). The residual roots had to be clinically and radiographically asymptomatic and covered by bone or healthy gingiva. The radiographic follow-up ranged from 20 months to 9 years. RESULTS: Healing was uneventful. Implants were clinically stable, and radiographic examination did not show any unusual feature at the root-implant interface. CONCLUSION: Several types of new implant-tissue interfaces were created in addition to the classical implant-bone interface, but this did not seem to jeopardize implant integration. Reports of more cases with a longer follow-up are needed before this protocol can be endorsed for routine application. Nonetheless, if confirmed as acceptable, this protocol might open intriguing possibilities; it might also lead to revision of one of the leading concepts in dental implantology.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".