Does a prosthodontist spend more time providing mandibular two-implant overdentures than conventional dentures?
Bibliographic record
Abstract
PURPOSE: In this article, the time taken by a prosthodontist to fabricate and maintain mandibular overdentures retained by two implants and conventional dentures is compared. MATERIALS AND METHODS: Sixty edentulous patients between the ages of 65 and 75 completed a randomized clinical trial. All received new maxillary conventional dentures and either a mandibular conventional denture (n = 30) or a two-implant overdenture on ball attachments (n = 30). The time spent by the prosthodontist and the number of visits required for treatment, including both scheduled and unscheduled visits, were recorded for each patient from preliminary impressions to 6 months following delivery. Data from the two groups were compared using Mann-Whitney U tests. RESULTS: The prosthodontist spent a mean total time of 296 minutes in treating an implant overdenture patient and 282 minutes on a conventional denture patient during the period from preliminary impressions to the 6-month follow-up. The mean numbers of appointments were 10.1 (implant group) and 10.8 (conventional group). These differences were not significant. CONCLUSION: Although additional knowledge is required to treat patients with implant prostheses, the time required by the prosthodontist to provide two-implant mandibular overdentures with ball attachments was not significantly different than the time needed for conventional denture treatment.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| 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".