Adoption issues associated with a new periodontal screening tool: an online survey of Canadian dentists.
Bibliographic record
Abstract
OBJECTIVE: To determine barriers and facilitators associated with the acceptance of a new diagnostic screening tool for periodontitis. METHODS: As part of a larger study to examine factors that affect the adoption of new technology by dentists, we piloted an online survey of Canadian dentists through an electronic newsletter produced by the Journal of the Canadian Dental Association. A new oral rinse that screens for the presence of periodontitis by estimating neutrophil abundance in saliva was used for illustrative purposes. The survey included questions about the types of patients for which the test would be beneficial, how the test might be incorporated into practice and how much the dentist would be willing to pay for the test. RESULTS: As the survey was delivered through a new communication tool, the response rate was low. Nonetheless there appeared to be interest in new periodontal screening tools to complement existing diagnostic tests for periodontitis. The test was seen as a valuable educational tool for patients; however, the cost to administer the test was determined to be an issue. CONCLUSIONS: Despite the low response rate, dentists were interested in new screening tests for periodontitis. A larger study with a more representative sample could provide valuable information for scientists who are interested in taking their research from the bench to chairside.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".