Overview of contemporary guidelines in digital pathology: what is available in 2015 and what still needs to be addressed?
Bibliographic record
Abstract
As technological advancements continue to transform the practice of pathology, new adopters of these technologies will look to guidelines on how best to incorporate them with an eye to preserving and enhancing patient safety and diagnostic quality. Telepathology, using a variety of digital pathology modalities, has tremendous potential to achieve that goal. Pathology departments are increasingly looking to implement different digital pathology platforms, whole slide imaging (WSI) systems in particular, for a broad range of applications in patient care. WSI allows for the acquisition, management and review of completely digitised slides as would be done with a light microscope. WSI also facilitates image analysis that cannot be carried out by a pathologist using traditional microscopy. Over the last few years, the Digital Pathology Association, The Royal College of Pathologists, College of American Pathologists, Canadian Association of Pathologists, the American Telemedicine Association and the Society of Toxicologic Pathology have published guidelines for validating and implementing digital pathology systems. This review summarises, compares and contrasts these published guidelines and discusses pertinent issues that need to be addressed as the guidelines are revised in the future.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".