A retrospective study on 1592 consecutively performed operations in one private referral clinic. Part I: Early inflammation and early implant failures
Bibliographic record
Abstract
BACKGROUND: Few large-scale follow-up studies are reported on routine implant treatment. PURPOSE: To report retrospective data on early inflammatory and early implant failures in a large number of routine patients at one private referral clinic. MATERIALS AND METHODS: A total of 1017 patients were consecutively provided with 3082 implants with an anodized surface (Nobel Biocare AB) at 1592 implant operations between 2000 and 2011. All patients reported with mucosa inflammation and bone loss and/or implant failures to the first annual examination were identified. A logistic multivariate data analysis was performed to identify possible factors with an association to the two events. RESULTS: Altogether 33 patients/operations presented early inflammation (2.1% operations). "History of periodontitis" (OR 3.91; 95% CI: 1.86-8.21), "numbers of implants" (OR1.33; 95% CI:1.07-1.67 per implant), "two stage surgical technique" (OR 3.70; 95% CI: 1.75-7.85), and "lower jaw" treatment (OR 4.73; 95% CI: 2.12-10.57) increased the risk for early mucositis with bone loss (P < .05). Highest risk for early inflammation was observed for patients at an age of 50-55 years at surgery (P < .05). "Smoking habits" (OR 2.08; 95% CI: 1.06-4.10) "Immediate implant placement" (OR 2.09; 95% CI: 1.23-3.54), and "immediate grafting procedures" (OR 2.09; 95% CI: 1.04-4.19) had a significant association to early implant failures (P < .05). Furthermore, risk for an early failure decreased with 22% per year of inclusion (2000 >2011; OR 1.22; 95% CI;1.08-1.39). CONCLUSION: History of periodontitis and two-stage surgery protocols with bone grafts in the (posterior) lower jaw increased the risk for early inflammatory problems after surgery (P < .05), with the highest risk for mid-aged patients (P < .05). Preventable factors related to the patient (smoking) and experience of surgeon showed to have a significant association to early implant failures in routine clinical practice (P < .05).
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".