Development and validation of digital medical laboratory educational platform
Notice bibliographique
Résumé
With the increased use of whole slide scanner technology, large numbers of tissue slides are being scanned and archived digitally. While digital pathology has substantial implications in telepathology, second opinions and education, there are huge research opportunities in this new type of digital data. Accessibility to large digital repositories of tissue slides is a huge educational resource for medical students and pathology residents. In addition to education of medical students, residents and clinical adoption, digital pathology has been transformative for computational imaging research. Many universities also do not have active pathology laboratories which are necessary in providing a steady flow of real life cases for medical training and research. Therefore, this project aimed at designing and validating a digital medical laboratory educational platform where learners could access the virtual slides from their individual student portals irrespective of their location when they logged in. With the use of a digital slide scanner, physical slides from the manual laboratory repository were scanned and stored in the cloud in a digital format. The two platforms of both institutions were integrated such that the students’ online learning portal was in sync with the Pathology Network’s digital repository. Once the integration was successful, the students were able to access and interact with the learning materials and virtual slides for Cytology and Cytological Stains which was the scheduled topic for teaching and learning. The study recruited thirty five students from Meru University of Science and Technology pursuing undergraduate studies in Medical Microbiology. Majority of the students (33 out of 35; 94.3%) indicated that digital pathology enhanced their understanding of the topic of study due to availability of virtual slides with ease and at any time of day. All students (35 out of 35; 100%) felt that digital pathology teaching was beneficial for Medical Microbiology students and expressed hope in continued learning using digital pathology to supplement face-to-face lectures. The study proved concept that digital pathology education is viable in medical training, particularly, for pathology and medical laboratory. There is need for additional work to include more areas in the field of laboratory medicine and development of virtual learning content. Such local digital slide repository can promote use of digital pathology in Kenya and the region for teaching and learning. Digital pathology together with virtual microscopy can progressively improve medical education and training of pathology and laboratory medicine.
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 | 0,010 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,002 |
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 ».