Exact correction factor for estimating the OR in the presence of sparse data with a zero cell in 2 × 2 tables
Why this work is in the frame
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Bibliographic record
Abstract
In case-control studies, odds ratios (OR) are calculated from 2 × 2 tables and in some instances, we observe small cell counts or zero counts in one of the cells. The corrections to calculate the ORs in the presence of empty cells are available in literature. Some of these include Yates continuity correction and Agresti and Coull correction. However, the available methods provided different corrections and the situations where each could be applied are not very apparent. Therefore, the current research proposes an iterative algorithm of estimating an exact (optimum) correction factor for the respective sample size. This was evaluated by simulating data with varying proportions and sample sizes. The estimated correction factor was considered after obtaining the bias, standard error of odds ratio, root mean square error and the coverage probability. Also, we have presented a linear function to identify the exact correction factor using sample size and proportion.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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