100 Creating a Multiplatform Educational Digital Slide Library Using Panoramic Digital Imaging System (Panoptiq): An Institutional Experience
Bibliographic record
Abstract
Digital slides provide a practical modality to enhance the way pathology residents and laboratory technicians can be educated. This is especially true in developing the visual pattern recognition skills required to decipher complex morphological features encountered in microscopy. Digital slides allow a user to access various learning tools via different electronic platforms at any location. Case slides were retrospectively selected for panoramic scanning that would provide the basic diagnosis or knowledge needed at the level of a trainee or technician. The slides were scanned by pathology residents using a microscope with an attached camera and the Panoptiq scanning software version 4.0 (ViewsIQ, Richmond, Canada). Where applicable, the software’s z-stacking function was used to view multiple plains of the slide. The digital slides were then uploaded to Panoptiq Portal (ViewsIQ.com), a cloud-based storage service and digital slide viewer. A total of 65 slides were scanned, amassing a total of about 11.6 GB, and were saved on a local hard drive and uploaded to the Panoptiq Portal. The portal also allowed for attaching clinical vignettes, gross images, and annotations to the slides. Residents were able to generate links to the scanned slides and share them via email, text messaging, and various social media platforms. Slides were capable of being viewed on multiple devices, such as desktop computers, mobile phones, and tablets. In the near future, a completely digital workflow may be on the horizon. The ViewsIQ software allows for an interactive educational experience where questions and comments can be left to be answered by any other viewer or educator. The utilization of this readily accessible training tool can provide educators an innovative way to reinforce and facilitate how pathology is taught.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.017 |
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".