Is an indistinct picture “exactly what we need”? Objectivity, accuracy, and harm in imaging for cancer
Bibliographic record
Abstract
Assumptions about the epistemic ideal of objectivity, closely related to ontological assumptions about the nature of disease as pathophysiological abnormality, lead us into oversimplified ways of thinking about medical imaging. This is illustrated by current controversies in the early detection of cancer. Improvements in the technical quality of imaging failed to address the problem of overdiagnosis in breast cancer screening and exacerbate the problem in thyroid cancer diagnosis. Drawing on Douglas and on Daston and Galison, I distinguish 3 dimensions of objectivity (accuracy, reliability, and precision) and demonstrate ways they may be at odds, as illustrated in the early detection of cancer. Guidelines for evaluating the efficacy of diagnostic imaging are insufficiently sensitive to this complexity. Improving imaging quality may raise epistemic issues, place disease definitions in question, and lead to overall harm or to changes in the distribution of harms and benefits among population subgroups. With a nod to Wittgenstein, I argue that we cannot take for granted that "an indistinct picture" is not "exactly what we need."
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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 | 0.074 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.084 |
| Scholarly communication | 0.013 | 0.038 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 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".