Cost Assessment of Epidemiologic Surveys in Dentistry
Bibliographic record
Abstract
Objective: The aim was to assess the relationship between the variability of data, sample size (n) and costs involved in epidemiologic surveys of dental caries. Research design and settings: In order to conduct this study, simulations of the variation in costs of hypothetic epidemiologic surveys were made and studied. Thus, all costs with reference to a survey were described and divided into two categories: fixed and variable. Outcome measures: The following margins of sampling errors were analyzed; 5%, 10%, 12% and 15% and the coefficients of variation (CV) of sampled data evaluated were, 50%, 80%, 100% and 120%. Results: The required sample size increased with the reduction in the margin of error. For a CV of 50%, considering an error of 5%, the sample size was 384; for the same CV and error of 10%, n was 96. Thus it was observed that the relationship of sample size between the errors of 5% and 10 % was 4 times higher. Whereas with regard to cost, when an error of 5% was adopted, this was approximately three times higher when compared with the error of 10%. Conclusion: Thus, when planning sample calculation, it is important to consider the Coefficient of Variation and the coherent errors with the variables under study, thus avoiding overestimating the sample and, consequently, increasing the costs involved in the research. It is fundamental to consider the possibility of working with other margins of error, thereby maintaining scientific strictness and establishing adequate costs.
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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 | 0.064 | 0.328 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".