Multivariate analysis of the influence of prosthodontic factors on peri‐implant bleeding index and marginal bone level in a molar site: A cross‐sectional study
Bibliographic record
Abstract
BACKGROUND: Peri-implant tissue condition can result from prosthodontic, surgical and bacteriological factors. PURPOSE: This study investigated the effects of prosthodontic factors on peri-implant tissue. MATERIALS AND METHODS: Subjects were 140 patients with 310 implants from Osaka University Dental Hospital. Prosthodontic factors examined were the connection type, the suprastructure retention type, the material of the abutment and the mesiodistal and buccolingual prosthetic form of the superstructure as emergence angle. The objective variables were the modified bleeding index (mBI) and marginal bone level (MBL). Statistical analysis was used as a generalized estimation equation. RESULTS: The taper joint had a significantly smaller MBL than the butt joint (P < .001). There was no significant difference in mBI and MBL between cement and screw retaining. Zirconium and titanium resulted in a significantly smaller mBI than gold alloy (zirconium/gold alloy: P = .037, titanium / gold alloy: P = .021), but there was no significant difference in the MBL. Both mBI and MBL tended to be smaller when the emergence angle was around 20° to 40°, although this difference was not significant. CONCLUSION: As a result of multivariate analysis, our findings suggest that to reduce MBL from the perspective of prosthodontic factors it is preferable to use an implant with a taper joint connection positioned with an emergence angle of 20° to 40°.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".