Dental implant failure rates with low insertion torque with a nonsubmerged surgical approach: A retrospective clinical study
Bibliographic record
Abstract
BACKGROUND: It is currently unclear if a low insertion torque (IT) should prompt a clinician to submerge the dental implant at time of placement. PURPOSE: This study aimed to analyze implant failure rates and marginal bone loss (MBL) as a function of IT and surgical approach. MATERIALS AND METHODS: A total of 197 patients who had received 295 Mozo Grau (MG) implants were included in this study. The healing of submerged or nonsubmerged implants was evaluated in regular IT (≥20-25 Ncm) or low IT (<20-25 Ncm) cases. Implant failure and MBL were evaluated before prosthesis placement and at 6 and 12 months after functional loading with generalized estimating equations. RESULTS: The overall 12-month implant failure rate was 4.8% (95% confidence interval [CI]: 2.7%-8.2%). When successful at 12 months, dental implants placed with low IT and nonsubmerging had the same MBL as implants dental implants placed with other approaches (mean difference = -0.02 mm; 95% CI -0.05 to 0.02). Low IT combined with nonsubmerging of the dental implant was associated with a 30-fold increased odds for dental implant failure (95% CI: 3.8-236.6). CONCLUSION: low IT and nonsubmerged healing was associated with a high failure rate.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".