Oral health technology assessment : study of mandibular 2-implant overdentures
Bibliographic record
Abstract
There is little evidence that Health Technology Assessment (HTA) is much used in dentistry. Dental implant technology is an example of innovative oral health technology. The objectives of this research were to gather the evidence needed for the assessment of overdenture implant treatment so that both patients and dental practitioners can make informed decisions about this technology. These objectives included 1) investigating what types of dental clinicians adopt and provide dental implants 2) determining the effect of the clinicians' experience in the provision of implant supported prostheses and 3) measuring the patients' preference in provision of mandibular 2-implants overdenture technology. For the first part, a cross-sectional survey was sent to all licensed Canadian Dentists to measure the adoption and provision of implant technology. For the second part, we used the data on the first 140 edentulous elders who were enrolled in a randomized controlled clinical trial to compare the effects of mandibular conventional (CD) and 2-implant overdentures (IOD) on nutrition. The change in patient ratings of satisfaction after treatment, laboratory costs and the number of unscheduled visits were compared. For the last part, edentulous elders (N=36) who were wearing maxillary dentures and either a mandibular conventional denture (CD, n=13) or a two-implant overdenture (IOD, n=23) participated in this study. Participants' preference was measured during a 20-minute interview. Multivariate regression analysis on the data from the first part of the study shows that the Dentist's gender, province of practice, specialty, and whether they practice alone or in association with other practitioners, are significantly associated with the adoption of implant technology (p<0.05). It is also shown that there was no difference in satisfaction scores for either prosthesis between the groups treated by experienced specialists or new dentists. Furthermore, it is shown that IOD wearers were willing to pay three times more than the current cost of conventional dentures for implant prostheses (p<0.05). Overall, the results of this study 1) inform decision makers on what types of clinicians provide implant technology and 2) suggest that, with minimal training, all dental clinicians irrespective of their specialty, can provide successful implant overdenture prostheses that edentulous patients are willing to pay for.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".