Development and validation of digital medical laboratory educational platform
Bibliographic record
Abstract
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.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".