Immediately Loaded Implants in Rehabilitation of the Maxilla: A Two‐Year Randomized Clinical Trial of Guided Surgery versus Standard Procedure
Bibliographic record
Abstract
BACKGROUND: Implant-simulation software can now be used to improve treatment planning, guide surgery, and ensure more accurate implant placement. PURPOSE: The aim of this study was to evaluate the outcome of a guided surgery protocol versus a conventional protocol. MATERIALS AND METHODS: Twenty-six patients were randomly assigned to Guided Surgery or Conventional Surgery. In test group implants were placed in the maxilla using a tooth supported model-based surgical guide with a minimally invasive flap and immediately loaded. In control group implants were inserted with an open flap surgery following a prosthetic stent and immediately loaded. RESULTS: A total of 70 implants were placed (36 test and 34 control). Statistically significant differences were found between the test group and the control group for patient opinion about self-confidence, assumption of analgesic tablets and perceived pain. The test group registered a statistically significant reduction (p < .05) as regards time of surgery and time of provisional insertion with respect to the control group. CONCLUSIONS: Implants can successfully integrate in the posterior maxilla using a guided surgery approach with immediate loading. The use of guided surgery helped to reduce the surgery duration, pain intensity, related analgesic consumption, and a more predictable provisional installation.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".