Bibliographic record
Abstract
In Reply.—We would like to respond to the various points in the letter by Greenberg.The letter obfuscates and misrepresents what we set out to do in terms of examining critically the strength of the “Helsinki Criteria” for attributing lung cancer to asbestos exposure. It does not discuss our specific examination of each of the criteria and appears to be more emotive than scientific. It does not provide any alternative criteria or explain why we are wrong in our criticisms.The letter implies that we said that lung cancers in asbestosis were “scar cancers.” This is not the case, rather the opposite. We consider that the cascade of events within the lung is diffuse and not focal leading to the inflammation-fibrosis-carcinogenesis process. This does not need to be proximate to the bronchial epithelium where the bronchial tumor arises. The growth factors and cellular events within the lung parenchyma are generalized and are initiated by the fibers deposited deep within the lung.Many of the epidemiologic studies that examined dose-response relationships between employment and the development of the lung cancer used employment records and industry sampling data. However, it was not appreciated at the time sampling was conducted that there were problems in assessing true airborne exposures by those methods. However, the authors of the Helsinki criteria did have modern measurements and up-to-date information to work with and, in our opinion, came to conclusions not supported by the data.The authors have no relevant financial interest in the products or companies described in this article.
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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.039 | 0.046 |
| Insufficient payload (model declined to judge) | 0.017 | 0.017 |
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".