A simple model for potential use with a misclassified binary outcome in epidemiology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
STUDY OBJECTIVE: Error in determination of disease outcome occurs in epidemiology, but such error is not usually corrected for in statistical analysis. A method of correction of risk estimates for misclassification of a binary disease outcome is developed here. METHODS: The method is a simple, closed form correction to the logistic regression estimate. A closed form variance estimate is also developed. SETTING: The method is illustrated in two studies, a cross sectional survey of cervicitis in Iran in 1996-97, as determined by inflammation on cervical smear specimens, and a case-cohort study of benign proliferative epithelial disease of the breast, in Canada 1980-88. MAIN RESULTS: The method provides corrected odds ratio estimates and corrects the spurious precision conferred by misclassification. CONCLUSIONS: The method is easy to apply and potentially useful, although potential failures of the assumptions involved should be borne in mind. It is necessary to give careful consideration to the plausibility or otherwise of the assumptions in the context of the individual study. Correction for misclassification of disease outcome may become more common with the development of readily applicable methods.
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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 | 0.145 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it