Malignant mesothelioma in females: the institutional failure by WHO and IARC to protect public health
Bibliographic record
Abstract
Malignant mesothelioma (MM) is a highly aggressive and difficult-to-diagnose tumor that is almost always caused by asbestos or other asbestiform fibers. Chapter 2 in the Fourth (2015) and Fifth (2021) Editions of the WHO/IARC Classification of Tumours is devoted to the classification and pathologic diagnosis of MM. The authors of these Chapters state that most of the cases of MM in females do not show asbestos as the cause when, in fact, the epidemiologic literature shows that the risk of MM in females exposed to asbestos approaches that in males. While it is correct that the overall incidence of MM in females is lower than in males, the view that MM in females is not caused by asbestos is unsupported. This view results from an inadequate occupational history, the failure to recognize the importance of environmental exposures, and the misrepresentation of published literature by the selection of limited literature and biased bibliographies, often by authors with financial conflicting interests. In this article, we present an example of the institutional failure (1) to protect the public health by permitting the publication of inaccurate statements about the adverse health effects of exposure to asbestos among females, and (2) to make suggested corrections that more accurately reflect reality. Responsibility for correcting the misinformation lies, in our assessment, both with the authors of the erroneous statements and with the editors and publisher of the books that contain these statements. At issue is nothing less than scientific accuracy, the fate of at-risk females for whom early diagnosis could result in improved health outcome, a missed opportunity to promote primary and secondary prevention, and the social injustice of the loss of compensation for females so affected. We describe the steps that we took to correct the inaccuracies, and to expose the dereliction of duty among responsible parties based, at least in part, on what we believe to be undisclosed conflicting interests. Our efforts failed.
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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 | 0.038 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.012 |
| 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; 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".