Accuracy and patient‐centered outcome variables in guided implant surgery: a <scp>RCT</scp> comparing immediate with delayed loading
Bibliographic record
Abstract
AIM: To assess the accuracy and patient-centered outcome of a novel guided surgery system for placing implants in an edentulous maxilla. MATERIAL AND METHODS: Fifteen consecutive patients with sufficient bone to place six implants in the maxilla were randomly assigned to the immediate loading (with delivery of the final prosthesis within 24 h) or the delayed loading treatment group. Accuracy was assessed by matching the planning CT with a postoperative CBCT. Patient-centered outcome measures were the Dutch version of the McGill Pain Questionnaire (MPQ-DLV), the health-related quality of life instrument (HRQOL), visual analog scales (VAS), the duration of the procedure, and the analgesic doses taken each day. RESULTS: A mean deviation was found at the entry point of 0.9 mm (range: 0.1-4.5, median 0.8) and of 1.2 mm (range: 0.2-4.9, median 1.1) at the apex, and an angular deviation of 2.7° (range: 0.0-6.6°, median 2.3) was observed. The mean vertical deviation was 0.5 mm (range: 0.0-3.2, median 0.4), and in a horizontal direction, this was 0.7 mm (range: 0.1-3.1, median 0.6). The mean deviation in mesio-distal direction was 0.5 mm (range: 0.0-2.3, median 0.4) and in bucco-lingual direction 0.5 mm ± 0.4 (range: 0.0-2.2, median 0.3). No statistical differences could be shown between treatment groups on pain response (MPQ-DLV), treatment perception (VAS), number or kind of pain killers, or for the HRQOLI instrument. CONCLUSION: The accuracy of a novel CT-based guide is comparable to the accuracy data of other systems. Within the limitations of this study, no difference could be found in patient-centered outcome variables after immediate or delayed loading.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".