Patient and implant‐related risk factors for implant failure of one‐stage lateral sinus floor elevation: A 2‐ to 10‐year retrospective study
Bibliographic record
Abstract
OBJECTIVES: This retrospective study aimed to evaluate the early and late implant failure rates of one-stage lateral sinus floor elevation (LSFE) and to identify the patient and implant-related risk factors associated with these failures. MATERIALS AND METHODS: All patients treated with one-stage LSFE from January 2014 to December 2021 were evaluated for inclusion. A total of 618 patients with 936 implants met the inclusion criteria. Clinical and radiographic information about patient and implants was collected. Univariate and multivariate Cox proportional hazards frailty regression models were performed to identify risk factors for early and late implant failure. RESULTS: The cumulative implant survival rate was 95.62% (95% CI 93.90%-97.68%), with 16 early implant failures and 25 late implant failures. The Cox analysis indicated that ≤3 mm residual bone height (RBH) was associated with a higher early failure rate. For late implant failure, smoking habit, ≤3 mm RBH, and certain implant brand were independent risk factors. Narrow sinus ostium, long infundibulum, and flat thickening of Schneiderian membrane might be non-independent risk factors for late implant failure. No significance was found in other variables, including age, periodontitis history, implant characteristics (position, diameter, length, protrusion length, marginal bone loss), surgeon experience, healing time, opposing dentition, and prosthesis. CONCLUSIONS: One-stage LSFE is a predictable treatment for patients with atrophic maxilla. ≤3 mm RBH increased the risk of early implant failure, while smoking habit, ≤3 mm RBH, and certain implant brand were independent risk factors for late 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".