One‐Year Follow‐up of an Implant with Early Radiographic Signs of Loss of Osseointegration: Case Report
Bibliographic record
Abstract
BACKGROUND: Failures in dental implant treatment are classified as early and late and depend on certain complications. Proposed preventions and treatment modalities for late complications have been comprehensively reviewed in the literature. Since early implant failures are characterized by lack of osseointegration, generally, removal of the implant is suggested to prevent extensive bone loss that would further complicate implant placements in future. PURPOSE: This case report presents the controversy between clinical findings and radiographic examinations in evaluating the nature of contact between an implant surface and the bone during the healing period. MATERIALS AND METHODS: In one patient scheduled for implant treatment in both jaws, four maxillary and two mandibular implants showed excellent healing, whereas one mandibular implant showed an extensive radiolucent area indicating early failure after 6 weeks of healing. The latter implant was clinically stable and was not removed. RESULTS: Radiographic evaluation 1 year later showed no sign of peri-implant radiolucency of the mandibular implant as seen after primary healing. CONCLUSION: When the bone-implant interface appears to be compromised radiographically but not clinically, such implants may have a favorable prognosis through a currently unknown biologic host response.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| 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".