Successful Treatment of Early Implant Failure: A Case Series
Bibliographic record
Abstract
BACKGROUND: The aim of this longitudinal study was to evaluate the effect of combined treatment on early progressive bone loss around dental implants. METHODS: The study sample consisted of 18 implants presenting at 4-6 weeks postplacement with early progressive bone loss. Clinical examination indicated the presence of a fistula in the soft tissue covering the implants in most cases. Defects around the implants were curetted, exposed implant surfaces were mechanically debrided and treated with tetracycline solution, and the defects were filled with bone graft and doxycycline powder. Bioabsorbable membranes were used. Final crowns were placed after 6 months. The patients were followed for an average of 30 months. RESULTS: The surgical sites healed without complication. At the time of loading, the defects were completely restored. At 12 months postloading, there was crestal bone loss to the level of the first thread (average, 1.3 mm). Pocket depths ranged from 3 to 5 mm (average, 3.6 mm) with no bleeding. No further changes were noticed throughout the remaining follow-up visits. All implants were successful according to the criteria proposed by Albrektsson and colleagues. CONCLUSIONS: Early detection and treatment of early progressive bone loss around dental implants are the key to saving early failing implants. The author recommends reevaluation visits 4-6 weeks postimplant placement to detect any signs of early failure so that immediate treatment can be undertaken if needed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| 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".