Development of a peak insertion torque prediction model for parallel-walled dental implants
Bibliographic record
Abstract
Implant peak insertion torque is a commonly used indication of primary stability that dentists rely on to make clinical decisions. The aim of this manuscript is to model the peak torque required for dental implant insertion based on clinical data such as bone properties, implant properties, and drilling procedure. A total of 116 parallel-walled Sweden and Martina dental implants were included in this study. Parameters such as age, sex, bone quality (derived from radiographs), applied peak insertion torque, implant location, implant length, final drill diameter, and implant diameter were recorded. Six data-driven regression models were trained and tested using different combinations of the clinical data to predict the peak torque. A physics-based model was also derived for the peak torque and compared with the data-driven models. The neural network model with early stopping achieved the best accuracy in predicting the clinically measured torque (R 2 = 0.7692, MSE = 0.08815). Within the limitations of this study, the results suggest that it is possible to predict the peak torque required for implant placement based on the patient's radiographs, implant's properties, and drill diameter. The findings of this study can serve as a reference for dentists in choosing drilling parameters for dental implant surgeries.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".