Bibliographic record
Abstract
Commentary on the paper by Reid et al (see page 509) Lung cancer is almost as dramatic a disease as is mesothelioma in its clinical course and prognosis. It has been a “rule of thumb” that there may be two asbestos related lung cancers for every mesothelioma, with a ratio of up to 10:1 in some heavily exposed occupational cohorts. Even in the United Kingdom, where mesothelioma deaths have risen so high they may have surpassed asbestos related lung cancer deaths,1 the latter remains estimated at 2–3% of all lung cancer. Due to disease time course, the potential effects of smoking cessation, and possibly improved screening of at-risk populations, lung cancer seems more amenable to early detection or prevention. Yet lung cancer receives far less attention, both scientifically and in the popular press. The problem is partly one of ease of attribution. For compensation boards and others charged with this task, this has proved more difficult for lung cancer. For mesothelioma, most—80% generally, and perhaps over 90% in the UK—are currently due to one factor. The potent associated influences of asbestos fibre type and of time from first exposure make deconstruction of the cause of individual cases of mesothelioma a straightforward matter given adequate information.2 Lung cancer has always presented less simple dilemmas. Approximately the same proportion of …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".