A preliminary comparison of masticatory performances between immediately loaded and conventionally loaded mandibular two‐implant overdentures with magnetic attachments
Bibliographic record
Abstract
BACKGROUND: Two-implant overdentures (2-IODs) is an expected treatment option to improve the masticatory performance (MP) of edentulous individuals. PURPOSE: The purpose of the current study was to compare MPs between immediately loaded and conventionally loaded mandibular 2-IODs retained by magnet attachments. MATERIALS AND METHODS: Nineteen participants with edentulous mandibles were randomly assigned to an immediately loaded group or a conventionally loaded group. Each participant received two implants. The immediate group was loaded on the same day as implant insertion, whereas the conventional group was loaded 3 months after implant insertion. Both MP, measured by a color-changeable chewing gum and a gummy jelly, and maximum occlusal force were assessed at baseline and at 1, 3, 6, 12, 24, and 36 months after implant insertion. RESULTS: A significantly higher MP, measured by gummy jelly, was observed at the 6-month time-point in the immediate group (P = .034). No significant differences in MP, measured by chewing gum, or maximum occlusal force were observed between the two groups at any time-point. CONCLUSIONS: Within the limitations of the present study, immediate loading of 2-IODs could improve MP at an earlier time-point than conventional loading of 2-IODs.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".