Pathogenesis, diagnosis and management of dentin hypersensitivity: an evidence-based overview for dental practitioners
Bibliographic record
Abstract
Though dentin hypersensitivity (DHS) is one of the most common complaints from patients in dental clinics, there are no universally accepted guidelines for differential diagnosis as well as selection of reliable treatment modalities for this condition. The neurosensory mechanisms underlying DHS remain unclear, but fluid movements within exposed dentinal tubules, i.e., the hydrodynamic theory, has been a widely accepted explanation for DHS pain. As several dental conditions have symptoms that mimic DHS at different stages of their progression, diagnosis and treatment of DHS are often confusing, especially for inexperienced dental practitioners. In this paper we provide an up-to-date review on risk factors that play a role in the development and chronicity of DHS and summarize the current principles and strategies for differential diagnosis and management of DHS in dental practices. We will outline the etiology, predisposing factors and the underlying putative mechanisms of DHS, and provide principles and indications for its diagnosis and management. Though desensitization remains to be the first choice for DHS for many dental practitioners and most of desensitizing agents reduce the symptoms of DHS by occluding patent dentinal tubules, the long-term outcome of such treatment is uncertain. With improved understanding of the underlying nociceptive mechanisms of DHS, it is expected that promising novel therapies will emerge and provide more effective relief for patients with DHS.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".