Bibliographic record
Abstract
To the Editor: Overdiagnosis challenges the assessment of the effect of environmental factors on the incidence of thyroid cancer.1 In fact, to identify the determinants of the trends of this cancer, the poster child of a “scrutiny-dependent” cancer,2 we need to move from classical epidemiology, which aims to understand patterns and causes of diseases, toward an epidemiology of diagnoses that aims to understand patterns and causes of diagnoses. Overdiagnosis is the diagnosis of an abnormality that is not associated with a substantial health hazard and of which people experience no benefit from awareness; it is not a misdiagnosis and not a false-positive result.3 Screening, incidental findings, increasingly sensitive screening and diagnostic tests, and widening diagnostic criteria to define a disease are causes of overdiagnosis.3 There is high-quality observational evidence from many countries that thyroid cancer overdiagnosis has a major impact on the incidence of this cancer.1,4 But this does not mean that no other factors have an effect on thyroid cancer incidence. As van Gerwen et al.1 have noted, several environmental factors could be involved in thyroid cancer trends. However, the effect of overdiagnosis on incidence can be so strong that it makes it difficult to identify environmental risk factors and quantify their contribution. Even in Japan following the Fukushima nuclear power plant accident, estimation of the effect of radiation—on top of the contribution of screening activities—remains a challenge.4,5 Hence, shortly after the accident in 2011, a screening program was implemented and a large number of thyroid cancers were found, including in children, raising major fear. Most cases were, however, probably overdiagnosed and not caused by the radiation because (1) the time lag between the accident and the detection of these cases was very short and (2) there was no correlation between the increase in incidence and regional levels of radiation exposure.5 The problem is that the absolute effect size of any environmental factors is most often small compared with the impact of overdiagnosis of such cancer. More broadly, thyroid cancer and other scrutiny dependent diseases (e.g., prostate cancer, melanoma, pulmonary embolism, or chlamydia infection) forces us to move from classical epidemiology toward an epidemiology of diagnosis. It means that the detection, screening, and history of diagnosis have to be documented in detail, in a population-based setting, for insightful analysis of trends.
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 | 0.007 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".