Avoidable burden of disease: conceptual and methodological issues in substance abuse epidemiology
Bibliographic record
Abstract
Determining the proportion of avoidable disease burden attributable to substance use is important for both policy development and intervention implementation. Current epidemiological theory has in principle provided a method to estimate avoidable burden of disease and the available statistical tools can provide first rough estimates. The method described in this paper, and its statistical procedures, are exemplified to estimate avoidable burden of tobacco-related disease in Canada. However, further effort is needed to find solutions in the methodological details, namely exposure measurement, risk factor multidimensionality, estimation of changes in exposure distribution over time, and estimation of risk relationships from multiple exposures changing over time with multiple endpoints (causal webs). The impetus to begin refining methods to obtain better starting points for estimating avoidable burden of disease is obvious and should be carried through in order to see real changes through evidence-based policy and intervention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.452 | 0.647 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".