Interproximal contact loss in a retrospective cross-sectional study of 4325 implants: Distribution and incidence and the effect on bone loss and peri-implant soft tissue
Bibliographic record
Abstract
STATEMENT OF PROBLEM: Interproximal contact loss (ICL) is a common finding between implant restorations and teeth, yet few reports have been published on incidence or related complications. PURPOSE: The purpose of this cross-sectional retrospective study was to measure the ICL of 4325 implants, including single and multiple splinted restorations. MATERIAL AND METHODS: Data on 4325 implants were extracted from patient records on ICL, time of follow-up, implant location, and sex of the participant for whom implants were placed in a private practice between 1999 and 2016. Periapical radiographs were used to evaluate the crestal bone level (CBL), whereas peri-implant soft tissues were evaluated with the implant mucosal index (IMI). Measurements (ICL, IMI, and CBL) were evaluated with an average follow-up of 4.5 years (range: 0.25 to 21 years). ICL was assessed in relation to the implant location and sex and grouped by the last clinical recall (1, 2-3, 4-5, 6-7, or 8+ years) to evaluate the effect of time. Data were analyzed by the chi-square test (α=.05). RESULTS: Overall, 17% of implants had ICL, and this significantly increased over time from 11% at 1 year to 29% at ≥8 years (chi-square: 123.8, P<.001). Mandibular implants had more ICL (20%) than maxillary implants (15%) (chi-square: 17.5, P<.001), whereas no difference was found between molar and premolar sites or male and female participants. There was no significant effect of ICL on CBL over time, but there was an increase in inflammation with higher IMI scores at ICL sites. CONCLUSIONS: The incidence of implant ICL was found to be 17%, and ICL was found to increase over time up to 27% at ≥8 years of follow-up. ICL was more common in posterior and mandibular sites. ICL was shown to increase soft tissue inflammation but was not found to affect implant CBLs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".