The influence of insertion torque values on the failure and complication rates of dental implants: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: The influence of using different insertion torque values on clinical and radiographic outcomes of implant therapy is unclear in the current literature. The aim of this systematic review and meta-analysis was to evaluate the implant outcomes and complications rates using high insertion torque values compared with those using regular insertion torque value levels. METHODS: Randomized controlled trials (RCTs), nonrandomized controlled clinical trials (NRCCTs), prospective and retrospective cohorts were searched for in electronic databases and complemented by hand searching relevant dental journals. The risk of bias was assessed using the Cochrane Collaboration's Risk of Bias tool for randomized and nonrandomized studies. Data were analyzed using a statistical software. RESULTS: A total of 718 studies were identified, of which, nine studies were included with 1229 dental implants in 684 participants. The meta-analysis of RCTs showed that the overall implant failure rate was not notably in favor of any insertion torque value and the difference between the two groups was not statistically significant (risk ratio 0.85; 95% confidence interval 0.07-10.52; P = 0.90). None of the RCTs was registered. The secondary analyses of non-RCTs did not either show any statistically significant difference. Overall meta-analysis did not show any significant differences in peri-implant marginal bone loss or biological/technical complications between high (≥50 Ncm) and regular insertion torque (<50 Ncm). CONCLUSIONS: There is insufficient evidence to support the use of high or regular insertion torque even with immediate implant restoration/loading. The short-term implant failure rates, changes in marginal bone level and complication rates were comparable when high or regular insertion torques were used for implant placement. The wide confidence interval indicated that results cannot be interpreted with clinically meaningful benefit for using either high or regular insertion torque.
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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 | 0.029 | 0.063 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.049 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".