Prognostic factors associated with implant loss, disease progression or favorable outcomes after peri‐implantitis surgical therapy
Bibliographic record
Abstract
BACKGROUND: The treatment of the peri-implantitis remains complex and challenging with no consensus on which is the best treatment approach. PURPOSE: To examine the key local and systemic factors associated with implant loss, disease progression, or favorable outcomes after surgical peri-implantitis therapy. MATERIALS AND METHODS: Records of patients treated for peri-implantitis were screened. Patient-, implant- and surgery-related variables on and prior to the day of the surgery were collected (T0: time of peri-implantitis treatment). If the treated implant was still in function when the data was collected, the patient invited to participate for a recall study visit (T1, longest follow-up after treatment). Impacts of the variables on the implant survival, success, and peri-implant bone change after treatment were investigated. RESULTS: Eighty patients with 121 implants with a mean follow-up of 42.6 ± 26.3 months were included. A total of 22 implants (18.2%) were removed during the follow-up period. When relative bone loss (%) was in range 25%-50%, risk for implant removal increased 15 times compared to lower bone loss <25% (OR = 15.2; CI: 2.06-112.7; p = 0.008). Similarly, relative bone loss of >50% increased 20 times the risk of implant failure compared to the <25% (OR = 20.2; CI: 2.42-169.6; p = 0.006). For post-treatment success rate, history of periodontitis significantly increased the risk of unsuccess treatment (OR = 3.07; p = 0.04) after resective surgery). CONCLUSION: Severe bone loss (>50%) poses significantly higher risk of treatment failure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".