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 …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.139 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".