Incidence of First Implant Failure: A Retroprospective Study of 27 Years of Implant Operations at One Specialist Clinic
Bibliographic record
Abstract
BACKGROUND: Even though there are many studies available reporting on implant failures, there are few studies that follow implant failures over time in large populations. PURPOSE: The purpose of this article is to present an overview of the annual incidence of reported implant failures for patients and operations over a 28-year period. MATERIALS AND METHODS: A total of 8,528 patients were consecutively provided with 39,077 implants in 10,719 implant operations during a 27-year period (1986-2012) at one specialist clinic. All patients with reported failures of implants during a 28-year routine follow-up period (1986-2013) were included, and data from the patients' files were retrieved and reported. RESULTS: Altogether, 857 patients (882 jaws/operations) were identified with one or more failures (10.0% of patients/8.5% of operations). Mean annual incidence of first failure showed obvious variations between years, even between seemingly clinically similar situations. However, incidence of first implant failure was higher for upper than lower jaws (p < .05), within 1 year of surgery (69%) than after 1 year (p < .05), and for implants with a turned surface compared with implants with a moderately rough surface (p < .05). CONCLUSIONS: With regard to annual failure incidence in relation to total number of operations over time, obvious variations in failure rate can be observed between seemingly similar clinical situations, as well as significant differences in incidence of first implant failure between the first year after surgery and later time points, between upper and lower jaws using implants with turned surfaces, and between operations to install implants with turned surfaces and those to install implants with moderately rough surfaces.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".