Data on implant failures will show different results depending on how patients are compiled and analyzed: A retrospective study on 3902 individual patients treated either with one single implant or implants in the edentulous upper jaw
Bibliographic record
Abstract
PURPOSE: The basic aim of the present study was to analyze the results of implant failures in two different implant populations, and how these results may vary depending on how the data are compiled and analyzed. MATERIALS AND METHODS: Two groups of consecutively treated patients were included who had been provided with either one single implant in a partially edentulous upper jaw (1881 patients) or four to eight implants in an edentulous upper jaw (2031 patients/12 454 implants). The risk of implant failure in the two groups separately and in combination was statistically compared by using uni- and multivariable analyses. RESULTS: The two groups showed significant differences in inclusion, surgical treatment protocols, and the risk of implant failures (P < .05). Overall, 25-year patient-level cumulative survival rates (CSRs) were 75.8% and 96.3% for edentulous and single implant treatment, respectively. "Dental condition" was the variable associated with the greatest risk of implant failure (HR 6.00; edentulous). Only one variable was significantly associated with the risk of implant failure in all tested groups ("time after surgery"; a decreased risk was observed over time), and more variables were statistically associated with implant failures in the edentulous group than in the single implant group. CONCLUSIONS: Edentulous patients present a significantly and substantially higher risk of implant failures than patients provided with a single implant. When patients with different clinical conditions are pooled into the same group, patients with the most common condition in the total group have greatest impact on the result of the total group. Based on the present observations, risk patterns for a certain oral condition are not necessarily comparable with the implant treatment received by other patients, and the external validity may be limited in small, homogeneous groups of patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".