Patient factors related to early implant failures in the edentulous jaw: A large retrospective<scp>case–control</scp>study
Bibliographic record
Abstract
BACKGROUND: Dental implants provide anchorage for dental prostheses to restore functions for individuals with edentulous jaws. During the healing phase, proper osseointegration is required to prevent early implant failure. More knowledge is needed regarding factors related to early failure of dental implants. PURPOSE: The aim of the present study was to identify possible risk factors for early implant failure, with respect to anamnestic and clinical parameters. MATERIALS AND METHODS: All patients with edentulous jaws with early implant failure (n = 408) from one referral clinic were compared with a matched control group (n = 408) with no implant failure. Early implant failure was identified during the first year of prosthetic function. Matching was performed on age, gender, year of surgery, type of jaw, and type of implant surface. In addition, data on anamnestic and clinical parameters were collected. The data were analyzed with a multivariable logistic regression model using early implant failure as the binary outcome. RESULTS: Five anamnestic factors were statistically significant with respect to higher probability for early implant failure: systemic disease, allergies in general, food allergies, smoking, and intake of analgesic medication. Four clinical conditions (i.e., implants in the opposing jaw, low primary stability, reduced bone volume, and healing complications) were also related to higher probability for early implant failure. CONCLUSIONS: This study identified nine factors associated with early implant failure, several related to patient's general health. Further investigations are needed to fully understand the causality between the obtained variables and early implant failure.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".