Smoking and dementia in male British doctors
Bibliographic record
Abstract
# Authors did not, strictly speaking, compare smokers with non-smokers {#article-title-2} EDITOR—Doll et al's finding that “persistent smoking does not substantially reduce the age specific onset rate of Alzheimer's disease or of dementia in general” is not surprising.1 The authors didn't compare smokers with non-smokers. By combining lifelong non-smokers and ex-smokers in the non-continuing group they effectively stopped comparing smokers with non-smokers. To complicate the issue further they then note, “As questionnaires were sent out only every six to 12 years, the mean time before death that the relevant smoking habits had been recorded was not 10 but 15 years.” In the end this study compares a group including non-smokers and ex-smokers who may have started smoking in the previous 15 years with a group of smokers who may have stopped in the previous 15 years. Has the BMJ fallen prey to the concerted and unrelenting efforts of health organisations determined to dictate an antismoking social policy rather than provide the honest and unbiased facts that people need to make informed personal choices? Or is the BMJ part of the team?
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".