Pathology and radiology taking medical ‘hermeneutics’ to the next level?
Bibliographic record
Abstract
Pathology and radiology are underpinned by image interpretation and analysis, using the most substratal fundamental yardstick of their function. Hermeneutics refers to interpretation (originally the interpretation of religious scriptures), especially the science and methodological principles of interpretation. The analysis and interpretation of images in pathology and radiology have been somewhat doctrine-driven, and using hermeneutics in a metaphorical manner in this context is perhaps appropriate. Radiologists and pathologists have clung to the romantic notion that they interpret the chiaroscuro of images presented before them, the tenebrosi of medicine. Recently, both pathology and radiology have been the subjects of discussion, with the application of artificial and/or alternative means being touted as potential replacement for medical specialists in both specialties. In November 2015, Levenson and colleagues wrote the provocatively titled paper: ‘Pigeons ( Columbia livia ) as trainable observers of pathology and radiology breast cancer images’.1 While the authors concluded that these volitant artistes of pathology/radiology image interpretation might ‘help us better understand human medical image perception’, the unwritten subtext and perception was that feathered, bird-brained purveyors of image interpretation do as good a good job as opposed to their cerebrally better endowed Homo sapien relatives. This publication led to a flurry of comments and jocular gybes directed at pathologists and radiologists chiefly aimed at them …
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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 | 0.016 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.073 |
| Scholarly communication | 0.019 | 0.035 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.018 | 0.027 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".