A History of the American Association of Pathologists’ Assistants: Creating an Organization, Winning Hearts and Minds, and Building an Invaluable Profession
Bibliographic record
Abstract
Thomas D. Kinney and Duke University started the first formal university-based training program for pathologists' assistants in 1969. Over the next 2 years, 2 more university-based programs were established. All 3 programs were affiliated with nearby Veterans Administration Hospitals and were funded as a pilot study by the US Veterans Administration to address a looming shortage of pathologists. Early graduates of these programs discovered that the concept of pathologists' assistants with well-defined skill sets encompassing both surgical and autopsy pathology was not initially accepted by important elements of organized pathology. Indeed, many academic pathologists were opposed to the concept from the outset. In the face of such opposition, a group of practicing pathologists' assistants created and incorporated their own professional organization, the American Association of Pathologists' Assistants, to provide support, advocacy, and continuing education for individual practicing pathologists' assistants. The history of the American Association of Pathologists' Assistants and its role in the establishment and success of the pathologists' assistant profession are described utilizing personal communications as well as published historical sources.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".