Training of novice surgeons using dynamic computer assisted dental implant surgery: An exploratory randomized trial
Bibliographic record
Abstract
BACKGROUND: Dynamic Computer Assisted Implant Surgery (CAIS) systems have been shown to improve accuracy of implant placement, thus training in the use of such systems is becoming increasingly important. There is a scarcity of research on how to implement dynamic CAIS training in the settings of postgraduate university education. PURPOSE: To determine the effectiveness of two modes of CAIS training programs on motor skill acquisition of novice surgeons. MATERIALS AND METHODS: Thirty-six postgraduate students without experience in dynamic CAIS systems were randomly assigned to a distributed training program (3 training sessions over 3 days) or a massed training (3 training sessions over the same day). A post-test involving the placement of one implant was conducted for both groups, 7 days after completion of the training. Surgical time and implant accuracy were recorded and analyzed, using independent t-tests, with 0.05 significant level. RESULTS: Both groups reached the accuracy benchmarks expected by current standards in the use of CAIS. No significant differences with regards to accuracy were found between the groups, but a trend was documented favoring performance of distributed (mean difference-0.4, 95% confidence interval-0.7-0.1) in the accuracy at platform level. Distributed training students performed faster than massed for the third trial (mean difference-95.0, 95% confidence interval-178.8 to -11.2). CONCLUSIONS: Novice students reached the accuracy benchmarks with the use of CAIS through both a massed and a distributed training program, while there was a strong but marginally not significant trend for higher accuracy in the distributed group. Students who received the training in the distributed format over the process of different days, performed faster. Trial registered in Thai Clinical Trials Registry: https://www.thaiclinicaltrials.org/show/TCTR20230109002. This clinical trial was not registered prior to participant recruitment and randomization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".