Overdiagnosis, ethics, and trolley problems: why factors other than outcomes matter-an essay
Bibliographic record
Abstract
In February 2014, the non-governmental Swiss Medical Board recommended that mammography programmes in Switzerland may eventually be closed down because they might not deliver more benefits than harms. In the resulting uproar the board was accused of being "unethical." Controversy about mammography has persisted in the UK, US, Canada, and elsewhere, and disputes about overdiagnosis exist in prostate cancer, chronic kidney disease, attention-deficit/hyperactivity disorder (ADHD), and many other conditions. People concerned about overdiagnosis are compelled by evidence of harms outweighing benefits. But not everyone is equally compelled. This may be because of disagreements over the evidence, conflicts of interest, or cognitive biases. Another possible cause of disagreement is that some people may not think that benefits and harms are the most important consideration. This contrast, between people who think outcomes are what matters most and people who disagree, is central to the discipline of ethics. It is a crucial difference between utilitarian ethicists and non-consequentialist ethicists. Broadly, utilitarians think that, given several options, we should choose the one that produces the best overall outcome (the most utility among the whole group of affected people), ensuring that each person counts equally in the calculation. Non-consequentialists don't consider outcomes to be so important: other ethical concerns, such as rights, duties, or respect for people's dignity or autonomy, matter more.
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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 | 0.035 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.051 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.018 | 0.028 |
| Insufficient payload (model declined to judge) | 0.002 | 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".