Surgical Management of a Severe Early Implant Complication: A 19‐Year Follow‐Up Case Report
Bibliographic record
Abstract
INTRODUCTION: Reports on long-term response to treatment of different implant complications with a span of more than 15 years are scarce. This case report presents a patient with early severe bone loss around an unloaded dental implant, with treatment and 19-year follow up. CASE PRESENTATION: A 60-year-old male non-smoker with no known systemic contributory history presented for replacement of the mandibular right first molar. The tooth was replaced with a titanium plasma-sprayed (TPS) implant using a non-submerged healing approach. Eight weeks post-surgery the patient reported discomfort in the area, followed by swelling, suppuration, and deep probing depths (PDs). A full-thickness flap revealed a bone defect that was thoroughly debrided until its deepest extension. The implant surface was scaled and subjected to air-powder treatment, followed by rubbing the TPS surface with a cotton pellet soaked in HCl-tetracycline. Guided bone regeneration was accomplished with use of an allograft followed by placement of a non-resorbable membrane. Follow-up after 19 years showed stability of the bone gain and reduction of PDs. CONCLUSION: The 19-year successful long-term result calls attention to the potential benefit of the combined anti-infective/regenerative approach and lasting effects of surgical management of early implant complications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".