Accuracy of static computer‐assisted implant placement in long span edentulous area by novice implant clinicians: A cross‐sectional in vitro study comparing fully‐guided, pilot‐guided, and freehand implant placement protocols
Bibliographic record
Abstract
BACKGROUND: To ensure accurate implant placement, surgical guides are used to control the steps of implant placement surgery. PURPOSE: Evaluation of the accuracy of implant placement in long span edentulous area by novice implant clinicians according to fully-guided (FG), pilot-guided (PG), and freehand (FH) placement protocols. MATERIALS AND METHODS: Maxillary surgical models with four missing teeth from the right first canine to the first molar were produced by 3-dimensional printing. Fourteen clinicians new to implant dentistry participated in the study, and each one of them inserted one canine and one molar implant for every implant placement protocol. All implant placement steps were completed in phantom heads to simulate the clinical situation. To evaluate the accuracy, the implant vertical, horizontal platform, horizontal apex, angle, and interimplant distance deviations from the planned positions were calculated. RESULTS: With the exception of vertical deviation, the FG placement was clearly more accurate than the PG and FH placements for all the variables for canine and molar implants. The PG placement was significantly more accurate than the FH placement for the horizontal platform and apex deviations, and interimplant distance deviation. The FG placement did not show a significant impact of the location of the implant, or the horizontal deviations of the platform or the apex. The PG and FH placements showed increased deviation at the canine implant than the molar implant, and at the apex of the implants than the platform of the implants. CONCLUSIONS: Within the limitations of this in vitro study, novice clinicians achieved a significantly more accurate implant position with FG placement, followed by PG and FH placements respectively. Therefore, a form of guided surgery is beneficial for novice clinicians.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".