Invited Commentary: Evaluating Epidemiologic Research Methods--The Importance of Response Rate Calculation
Bibliographic record
Abstract
Epidemiologic research that uses administrative records (rather than registries or clinical surveys) to identify cases for study has been increasingly restricted because of concerns about privacy, making unbiased population-based research less practicable. In their article, Nattinger et al. (Am J Epidemiol. 2010;172(6):637-644) present a method for using administrative data to contact participants that has been well received. However, the methods employed for calculating and reporting response rates require further consideration, particularly the classification of untraceable cases as ineligible. Depending on whether response rates are used to evaluate the potential for bias to influence study results or to evaluate the acceptability of the method of contact, different fractions may be considered. To improve the future study of epidemiologic research methods, a consensus on the calculation and reporting of study response rates should be sought.
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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 | 0.031 | 0.147 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.065 | 0.052 |
| Insufficient payload (model declined to judge) | 0.007 | 0.012 |
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".