To what extent can attributable fractions in occupational epidemiology be estimated in the absence of key data?
Bibliographic record
Abstract
In a recent paper, Ghoroubi et al. (Am J Epidemiol 2025 Jan 8;194(1):302-310) used the indirect attributable fraction (AF) method to provide estimates of fractions of all-cause mortality attributable to work-related factors. This commentary discusses the limitations and potential of this paper and provides insights and guidance to make optimal use of indirect AF estimation in occupational epidemiology. The crucial steps are the choice of the datasets and input data related to the prevalence of exposure and relative risk (RR), requiring comparability of time period, population characteristics, and the definition and measurement of exposure. Published systematic literature reviews with meta-analyses are essential or, if not available, conducting meta-analyses to provide estimates of RR. Finally, it is important to verify the assumptions for the chosen AF formula including evidence of causality, consideration of confounding and (in)dependence between exposures when several exposures are studied at the same time. We conclude by suggesting that the paper by Ghoroubi et al. may have provided a proof of concept for 1 work-related factor only, but considerable additional research will be required to represent work-related factors overall.
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 | 0.211 | 0.629 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.009 | 0.023 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".