Illustrating Medicine: Line, Luminance and the Lessons from J.C.B. Grant’s <i>Atlas of Anatomy</i> (1943)
Bibliographic record
Abstract
The onset of the Second World War created a temporary crisis in the North American medical community when the supply of medical textbooks from Europe, used to train physicians and surgeons, was threatened. In 1941, Dr J.C.B. Grant of the University of Toronto proposed a new anatomical atlas, comprising both tonal and line drawings, to address this need. In this visual essay, the authors briefly illustrate Grant’s method for creating these drawings, and his systematic and deliberate use of photography in the process. They explain the reasons for Grant’s use of black and white images, and examine the specific illustration techniques used by these artists. A series of close-ups of the original drawings produced for the Atlas in the 1940s highlight the visual communication strategies deployed by these skilled illustrators. In so doing, they make an argument for the importance of examining how images are produced for medical publication, and not merely examining what is produced.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".