Additional file 1 of Trends in mortality from alcohol, opioid, and combined alcohol and opioid poisonings by sex, educational attainment, and race and ethnicity for the United States 2000–2019
Bibliographic record
Abstract
Additional file 1: Fig. S1. Total number of deaths aged 18 or older in US 2000-19 from alcohol poisoning only, opioid poisoning only and alcohol and opioid poisoning using three different versions of alcohol poisoning definition. Fig. S2. Age-standardized mortality rates for alcohol poisoning, opioid poisoning and combined alcohol and opioid poisoning for men and women by race and ethnicity categories from 2000 to 2019. Table S1. ICD-10 Codes used to define alcohol and opioid poisoning cause-of-death. Table S2. Number of deaths by three versions of alcohol poisoning definition (raw, adjusted and final) for alcohol poisoning only, opioid poisoning only and alcohol and opioid poisoning, for the total population, age 18 or older, and age 25 or older. Table S3. Coefficient estimates of generalized least square (GLS) models predicting racial and ethnic and educational differences in US poisoning mortality rates (per 100,000) aged 18 and over 2000-2019. Table S4. Coefficient estimates of generalized least square (GLS) models predicting racial and ethnic differences in educational inequalities in US poisoning mortality ratios calculated from mortality rates (per 100,000) aged 18 and more 2000-2019. Table S5. Coefficient estimates of random-effect Poisson models predicting racial and ethnic and educational differences in US poisoning death counts aged 25 and more 2000-2019.
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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 | 0.001 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.740 | 0.129 |
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".