Predicting Implant Failure and Complications Using Cluster Analysis After Variable Selection: A Retrospective Study
Bibliographic record
Abstract
BACKGROUND: Uneven data distribution (due to rare outcomes) and repeated measurements (from multiple implants per patient) hinder the creation of a precise oral implant failure risk model. PURPOSE: The aim of this study was to explore variable selection methods suitable for oral implant data, assess risk factors of early failure and postoperative complications, and apply the two-step cluster analysis to establish a risk prediction model for oral implant failure, providing a reference for clinical practice. MATERIALS AND METHODS: This study was a retrospective analysis, with early failure and postoperative complications serving as the outcome indicators. Given the repeated measurements and uneven distribution in oral implant data, our study conducted a comparative analysis between GEE and GEE with Firth penalization. This study evaluated the influencing factors screened by a more suitable model and utilized them for subsequent risk prediction. A two-step cluster analysis was applied to identify different subgroups of early failure and postoperative complications; their clinical characteristics were compared, and relevant risk prediction models were developed. RESULTS: Among a total of 677 patients and 1200 implants, 21 implants were lost prior to loading, and postoperative complications occurred in 74 patients involving 94 implants. The GEE model with Firth's penalty term indicated that non-submerged healing (p < 0.001), shorter implant length (p < 0.001), and thinner diameter (p = 0.007) were risk factors for early failure. The GEE model showed that non-submerged healing (p = 0.039) was a protective factor against postoperative complications, whereas unhealed extraction sockets at the implant site (p = 0.048), the use of bone substitutes (p = 0.008), and a history of periodontal disease (p = 0.009) were risk factors. Additionally, the use of bovine tendon-derived absorbable biomembranes (p = 0.036) may elevate the risk of postoperative complications. The two-step cluster analysis identified two patient subgroups, categorized as high-risk and low-risk, and the prediction model demonstrated good discrimination ability. CONCLUSIONS: Early failure data were highly imbalanced, and the incorporation of the Firth penalty term provided significant benefits. However, its effectiveness in managing postoperative complication data remained limited. Thus, a one-size-fits-all approach to variable screening may not have suited all types of imbalanced data. The analysis conducted in this study, using specific screening techniques, yielded more reliable influencing factors. Additionally, the developed two-step clustering model was capable of predicting high-risk patients for early failures and postoperative complications before surgery, aiding clinicians in devising personalized preventive measures to reduce incidence rates. TRIAL REGISTRATION: Clinical trial registration number: ChiCTR2300070420.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".