Choosing or refusing oral implants: a prospective study of edentulous volunteers for a clinical trial.
Bibliographic record
Abstract
PURPOSE: Little is known about why people accept or refuse oral implant treatment. The purpose of this study was to assess edentulous subjects' acceptance or refusal of free implants to retain mandibular dentures, and to evaluate factors that might predict those who are more likely to choose implants. MATERIALS AND METHODS: One hundred one volunteers completed questionnaires about their background, satisfaction with conventional dentures, oral health-related quality of life, and preference for implants. Results were analyzed using Pearson chi-square tests and logistic regression. RESULTS: While 79% of volunteers accepted and 21% refused an initial offer of free implants, a number of them changed their minds, leaving 64% who wanted implants and 36% who did not want them. The most common reason for choosing implants was anticipation of improved mandibular denture stability or security (73%), while the most common reason for refusal was concern about surgical risks (43%). A logistic regression model identifying those who complained of poor chewing function, poor speech, pain, and dissatisfaction with appearance improved the prediction of those who wanted implants from 64% to 80%. CONCLUSION: When cost was removed as a factor, more than one third (36%) of the older, edentulous participants in this study ultimately refused an offer of free implants to retain their mandibular dentures. Poor chewing function, poor speech, pain, and dissatisfaction with appearance were the most important factors in predicting who would choose implants.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".