Survey methods contributing to the difference of dentin hypersensitivity prevalence among publications between 1998 and 2022: a research-on-research study
Bibliographic record
Abstract
BACKGROUND: The prevalence of dentin hypersensitivity (DH) differed significantly among previous reports, which may confuse clinicians and public health practitioners. This study aimed to identify which survey methods contributed to the difference in reported DH prevalence. METHODS: A systematic search was performed in Medline, Embase, ProQuest, CNKI, and ClinicalTrial.gov databases up to November 2022. Two authors extracted the basic characteristics and survey methods independently. A random-effect meta-analysis was performed to estimate the effects of survey methods on estimated DH prevalence. The Newcastle-Ottawa Scale (NOS) was employed to appraise the methodological quality of the studies included in the analysis. RESULTS: = 99.7%, P < 0.001), especially in the field of survey methods. Variables were observed in sampling approaches, study settings, and inclusion criteria. Besides, clinical examination protocols and reporting of inter-examiner reliability remained inconsistent. Meta-regression analysis showed that the DH prevalence might be underestimated when the clinical examinations were conducted only for participants with positive subjective symptoms (P = 0.001). The included studies scored 5.74 ± 1.7 on the NOS, indicating relatively low methodological quality. The lower study quality was primarily attributed to insufficient elaboration on representativeness of the exposed cohort, comparability of cohorts on the basis of the design or analysis controlled for confounders, and follow-up procedures. CONCLUSION: The included studies demonstrated substantial heterogeneity in survey methods. Conducting clinical examinations for all participants enhanced detection rates. The reliability of our pooled prevalence estimates was substantially compromised due to the studies' low methodological quality and high heterogeneity. It is recommended to propose the instructive detailed guideline to standardize the design and improve the quality of studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".