Early Implant Failures Related to Individual Surgeons: An Analysis Covering 11,074 Operations Performed during 28 Years
Bibliographic record
Abstract
Abstract Background Compared with knowledge on patient and implant component factors, little knowledge is available on surgeons' role in early implant failures. Purpose To report incidence of early implant failures related to total number of operations performed by individual surgeons. Materials and Methods Early implant failures (≤1 year of implant prosthesis function) were reported after a total of 11,074 implant operations at one specialist clinic during 28 years of surgery. Altogether, 8,808 individual patients were treated by 23 different dentists, of whom 21 surgeons were specialists in oral surgery or periodontology. Recorded failures were related to total numbers of performed operations per surgeon, followed by statistical comparisons (χ2) between surgeons with regard to type of treated jaw and implant surface. Results Altogether, 616 operations were recorded with early implant failures (5.6%), most often observed in edentulous upper jaws after placing implants with a turned surface (p < .05). Significant differences between surgeons, gender of surgeon, type of treated jaws by the surgeon, and implant surface used by the surgeon could be observed (p < .05). Conclusions Early implant failures are complex, multifactorial problems associated with many aspects in the surgical procedure. A stochastic variation of failures for individual surgeons could be observed over the years. Different levels of failure rate could be observed between the surgeons, occasionally reaching significant levels as a total or for different jaw situations (p < .05). The surgeons reduced their failure rates when using implants with moderately rough surfaces (p < .5), but the relationship of failure rate between the surgeons was maintained.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".