The Beginnings of Pathology in America: A Contemporary Analysis of William E. Horner's A Treatise on Pathological Anatomy
Bibliographic record
Abstract
CONTEXT: A Treatise on Pathological Anatomy, published in 1829 by William E. Horner, is the first American textbook on pathology. Several articles have been written on Horner, but they do not evaluate the role that the knowledge he recorded played on the intellectual origin of the discipline of pathology in America. Only one article, published in 1930, deals in some detail with the content of the Treatise. Because of new historiographic standards, this is an opportunity to expand on, and update, that article. Furthermore, Horner's book is now available free online, and print-on-demand paperback copies can be ordered for a modest cost from online booksellers. OBJECTIVE: To describe the organization and structure of the scientific knowledge found in the Treatise with the intent of demonstrating how this material created the intellectual basis for the origin of pathology as a discipline in America. DESIGN: Using current historiographic standards, the knowledge included in the book is examined and contextualized within the social, professional, and educational conditions existing at the time of publication. The essay also includes biographic data on the author. RESULTS: The Treatise contains important information on the principles, ideas, and practice of pathology in the nineteenth century and illustrates the influence of French literature on the author. CONCLUSION: The contribution of the Treatise as the first formal textbook on the subject in America is seminal and should be the basis for further historic studies on the organization and structure of scientific knowledge in pathology in America.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".