A retro‐prospective effectiveness study on 3448 implant operations at one referral clinic: A multifactorial analysis. Part II: Clinical factors associated to peri‐implantitis surgery and late implant failures
Bibliographic record
Abstract
BACKGROUND: Little knowledge is available on implant treatment in large effectiveness studies in routine practice. PURPOSE: To report retro-prospective data on prevalence of peri-implantitis surgery and late implant failures in a large number of routine patients at 1 referral clinic. MATERIALS AND METHODS: Altogether 9582 implants with an anodized surface (Nobel Biocare AB) were consecutively placed between 2003 and 2011 and followed-up to end of 2015. All peri-implantitis operations and late implant failures were consecutively identified. A logistic multivariate data analysis was performed to identify association between the complications and different clinical factors. Furthermore, data on prevalence on risk for inflammation and bone loss at implants ("peri-implantitis") and surgery related to peri-implantitis was collected for another reference group of about 10 000 implant patients during 3 consecutive years (2013-2015). RESULTS: Cumulative survival rates for implant operations without peri-implantitis surgery or implant failures were calculated to 96.4% (95% CI: 97.3-95.4) and 95.0% (95% CI: 96.0-94.1) after 10 years, respectively. Risk for "peri-implantitis surgery" showed a significant association (P < .05) to number of placed implants (hazard ratio [HR] 1.40; 95% CI: 1.24-1.59). Three factors showed significant association to risk for "late implant failures," where "treatment in lower jaw" had the highest risk; HR 2.03. "Overall implant failures" were associated to 4 significant factors where "surgeon" (HR 2.50) showed highest impact on risk. "Numbers of implants" and "bone resorption" at surgery were the 2 significant factors that were consistent for all the time periods of failures during follow-up (early/late/total). On an average 7.4% of examined patients in the reference group were denoted with highest risk group ("peri-implantitis") of which on an average 12.7% of these patients had surgery related to peri-implantitis. CONCLUSIONS: "The dentist" involved in the surgical and prosthetic rehabilitation of the implant patients, "number of implants" and degree of "bone resorption" seem to have most impact on overall implant complications and failures in the present patient group.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.003 | 0.001 |
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".