Factors Associated with Failure of Surface‐Modified Implants up to Four Years of Function
Bibliographic record
Abstract
OBJECTIVES: The relative impact of innovative treatment concepts on the failure of surface-modified implants is not well understood. This retrospective study aimed to explore this using data obtained in a university postgraduate training center. MATERIAL AND METHODS: Patients treated with implants for a variety of indications over a 3-year period were included. All implants had been at least 1 year in function. Clinical records were evaluated for implant failure and in reference to implant length/diameter/location, time from tooth loss to implant placement, bone condition (native/grafted), surgical protocol (two-/one-stage), loading protocol (delayed/early/immediate), type of prosthesis (removable/fixed), surgeon's experience level (resident/trainee) and specialty (periodontist/oral surgeon). The impact of each covariate on failure was tested using the Fisher's exact test. Kaplan-Meier survival functions were constructed and Mantel-Cox log-rank tests were used to compare survival functions. To correct for possible interaction, Cox proportional Hazards regression was adopted. RESULTS: Forty-one of 1,180 (3.5%) implants were lost in 34/461 (7.4%) patients (245 ♀, 216 ♂; mean age 51, range 18-90). Factors showing significant impact on failure on the basis of univariate analyses were implant location (p = .015), surgical protocol (p = .002), loading protocol (p = .002), surgeon's experience level (p = .035) and specialty (p = .001). When controlling for other covariates, only the loading protocol had a significant influence (p = .049) with early loading more prone to failure (p = .014) when compared with delayed loading. Immediate loading and delayed loading showed comparable implant survival (p = .311). CONCLUSIONS: Implant therapy may be highly successful in a training center where inexperienced clinicians are strictly monitored and personally guided. Implant specific variables do not affect implant survival but early loading is a risk indicator for implant failure, whereas immediate loading is not.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".