The risk factors of early implant failure: A retrospective study of 6113 implants
Bibliographic record
Abstract
BACKGROUND: The risk factors of early implant failure were controversial among previous studies, especially for implants in different sites. PURPOSE: To analyze the rate and risk factors of early implant failure occurring before the placement of final prosthesis. MATERIALS AND METHODS: A retrospective study was conducted based on electrical medical records of patients who received dental implant placement from 2015 to 2019. Generalized estimation equation analyses were used to explore potential risk factors influencing early implant failure. RESULTS: Overall, 6113 implants in 3785 patients were included. The rate of early implant failure was 1.6% at patient level and 1.2% at implant level. The early implant failure was significantly associated with implants in the posterior maxilla, with specific surface modifications and in previously augmented sites (p < 0.05). Risk factors for maxillary implants included surface modification and bone augmentation procedures (p < 0.01), whereas risk factors for mandibular implants included gender and bone augmentation procedures (p < 0.05). For implants placed in previously augmented sites, implants placed in the anterior mandible had a higher risk of early failure (p < 0.05). CONCLUSIONS: The risk factors for early implant failures varied among different sites; hence, they should be comprehensively considered in presurgical treatment plan.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".