Additional file 1 of The impact of alcohol taxation increase on all-cause mortality inequalities in Lithuania: an interrupted time series analysis
Bibliographic record
Abstract
Additional file 1: Figure S1. Monthly death count across the study period by sex, age group and education. Figure S2. Monthly population count (person-years) across the study period by sex, age group and education. Figure S3. Monthly age-standardized mortality rates across the study period by sex and education. Figure S4. Monthly death count of lower educated persons for 2011 compared to all remaining years, by sex and age group. Figure S5. Cross-correlation of potentially confounding variables with absolute mortality difference among men. Figure S6. QQ-plots of dependent variables. Figure S7. Time series of the four dependent variables (mortality difference and ratio by sex). Table S1. Source and availability of potentially confounding variables. Table S2. Correlation of potentially confounding variables with mortality inequalities (dependent variables). Table S3. Baseline model selection for time series of n=71 months between April 2011 and February 2017. Table S4. Main model selection – mortality difference. Table S5. Main model selection – mortality ratio (logarithmized). Table S6. Cause of death groupings. Table S7. Changes in mortality inequalities (absolute difference in age-standardized mortality rate) by cause-of-death grouping for men.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.408 | 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".