Bibliographic record
Abstract
The unwavering commitment of medical educators inspired the creation of my artwork Arscience, on the cover of this issue. Their dedication and wisdom to empower future generations of clinicians have ultimately shaped my values as an aspiring medical educator. Within my own teaching practices as an anatomist, I often refer to clinical correlations between gross and histological characteristics in different diseases. Histology is the study of the microscopic anatomy of cells and tissue. At the first sight of stained slides, histology can seem dull. But as a medical researcher and educator, I believe these images are not just a form of supporting evidence for doctors’ practice but also a form of art in science. Through a series of labor-intensive processes—from tissue sampling to performing lab tests—one will learn to appreciate these stained slides and their immense contribution to informing medical care.ArscienceIn doing so, I hope my students gain insight into the depth and breadth of pathology through the lens of both macroscopic and microscopic counterparts. That is, the proper diagnosis of diseases relies on both a holistic presentation of symptoms (discernible with the naked eye) and evidence from the cellular level (through the lens of technological aids). For this reason, I chose histology to be the central theme of my artwork to convey the following key messages: (1) Medicine is a collective effort between patients and health care staff from different disciplines to ensure optimal care, and (2) accurate visual presentations of medicine allow experts and the general public to understand the causes of diseases. Consequently, I titled my painting Arscience, a compound word of Latin terms: “ars” and “scientia,” meaning “art” and “science,” respectively. To reflect the extensive history of pathology evolution, I chose to use traditional art techniques and media from Chinese painting. Chinese painting (also known as “guohua” or “native painting”) is one of the oldest art forms in the world,1 symbolic of the long-standing history pathology has had in medicine. I also chose to use contemporary Chinese painting styles to reflect the diversity of my upbringing as visual minority and the value of cultural competency—to understand and acknowledge individual differences in how people react to illnesses. To achieve this aim, I used 2 major techniques of Chinese painting: gongbi and shui-mo. Gongbi uses highly detailed and well-controlled brushstrokes that delimit details with precision, and shui-mo, or ink-wash painting, involves watercolor or brush painting with black ink and colored pigments. Altogether, these elements created a fusion piece of artwork that illustrates a diagnostic method that is fundamental in contemporary medicine and health research. Inspired by histological slides that have embedded heart-shaped structures, I hope my art will also inspire the readers of Academic Medicine to bear a compassionate heart for ill patients and their families, along with dedicated medical professionals and researchers who have committed their lives to the well-being of others. Each quadrant—from left to right and top to bottom—represents a histology stain from different anatomic locations: specimens from a bronchiole, colonic crypts, pancreas, and thyroid follicles.
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 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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.292 | 0.132 |
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".