Implant failures and age at the time of surgery: A retrospective study on implant treatment in 2915 partially edentulous jaws
Bibliographic record
Abstract
PURPOSE: To further report and analyze data on the prevalence of implant failures related to age at surgery in partially edentulous jaws. MATERIALS AND METHODS: Altogether, 2915 partially edentulous jaws (Kennedy Class I and II) were consecutively treated with 9167 implants over a 30-year period (1986-2015) in one referral clinic. All implant failures were consecutively recorded and the first event of implant failure was analyzed in relation to patient age at implant surgery. RESULTS: A total of 2453 patients participated in the study. The main observations were a nonlinear, normal distribution association between risk of implant failure and age at surgery with the highest risk in middle-aged patients. The risk for implant failures was significantly higher for middle-aged patients (45-64 years) than for old patients at the time of surgery (P < .05). The overall cumulative survival rates for treated jaws increased consistently from the age group of 40 to 49 years to that of >79 years. However, younger age groups (<40 years) presented a different pattern. Partially edentulous patients included late in the study (2003-2015) presented a more pronounced nonlinear, normal distribution, and the highest risk of implant failure in patients between 50 and 55 years of age at surgery. CONCLUSIONS: An overall nonlinear risk pattern of implant failure was observed, with the highest risk in the middle-aged group at implant surgery. Overall cumulative survival rates were highest in the youngest and oldest age groups at implant surgery, and this pattern became more pronounced in patients included late in the study.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".