Tooth hypersensitivity treatment trends among dental professionals.
Bibliographic record
Abstract
OBJECTIVE: Tooth hypersensitivity is a common complaint of patients who present to the dental office. The aim of this study was to survey dental professionals in an effort to understand the current treatment trends for tooth hypersensitivity. METHOD AND MATERIALS: A questionnaire that addressed possible treatments for tooth hypersensitivity was developed and validated. The survey included a case presentation in which the responders were requested to list a first and second line of treatment. The questionnaire was distributed to dental professionals and analyzed statistically. RESULTS: A total of 106 questionnaires were collected. The most common first line treatments for tooth hypersensitivity included sensitivity toothpastes (38.7%) and desensitizers (40.6%). Referral for patients with tooth hypersensitivity was indicated by 12.0% of the responders. The most preferred products included sensitivity toothpaste (34.9%) and fluoride varnish (19.8%). In regards to the case presented in the survey, the most common first treatment recommendations for patients were to use a sensitivity toothpaste (37.7%), stop drinking cold water (13.2%), and apply a desensitizing agent (23.6%). Of the 106 responders, 7.5% would opt to graft the recession area and 29.2% would restore the area as the second line of treatment. CONCLUSION: This study suggests that more invasive treatment options such as grafting and restoring may be used too early in the treatment plan for tooth hypersensitivity. Providing continuing education programs that address simple and less aggressive or invasive modes of treatment will benefit patients and may also reduce the financial burden of the 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".