Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,008 | 0,004 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,292 | 0,132 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».