Does flap opening or not influence the accuracy of semi‐guided implant placement in partially edentulous sites?
Bibliographic record
Abstract
OBJECTIVE: To investigate the effect of open-flap or flapless approaches on the accuracy of implant placement partially guided by tooth-supported surgical templates. MATERIALS AND METHODS: A total of 36 edentulous sites were selected from seven human cadaver heads. Following the preoperative implant planning using Blue Sky Plan, surgical guides were fabricated by an in-office desktop 3D printer. All the sites were randomly divided into two groups: flapless approach (n = 18), and open-flap approach (n = 18). After guided osteotomy preparation with subsequent freehand implant placement, digital intraoral scanning was performed to obtain post-operative implant positions. Based on the image registration, the deviations between the planned and actual implant position were measured and compared. RESULTS: Statistically significant variance differences between the two approaches were found in the global coronal (open-flap: 0.86 ± 0.23 mm; flapless: 1.3 ± 0.62 mm; P < .001), global apical (open-flap: 1.38 ± 0.37 mm; flapless: 1.9 ± 0.78 mm; P = .002), and depth (open-flap: 0.59 ± 0.34 mm; flapless 0.89 ± 0.78 mm; P < .001) deviations. The differences were not significant regarding lateral (coronal and apical) and angular deviations. CONCLUSIONS: In semi-guided implant surgery, the open-flap and flapless approaches demonstrate similar lateral and angular deviations. The open-flap group shows better depth control when manually inserting the implant.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".