American Telemedicine Association clinical guidelines for telepathology
Bibliographic record
Abstract
The term “telepathology” was introduced into the English language in 1986 by Weinstein,[1,2] and since then there have been many advances and publications.[3,4,5,6,7,8,9,10,11,12,13] The practice of telepathology involves obtaining macroscopic and/or microscopic images for transmission along telecommunication links for obtaining a remote interpretation (telediagnosis), second opinion or consultation (teleconsultation), quality assurance, education, teaching, self-study, and research (tele-education). A variety of terms has been used interchangeably to refer to telepathology including digital microscopy, remote robotic microscopy, teleconferencing, teleconsultation, telemicroscopy, video microscopy, virtual microscopy, and whole slide imaging (WSI).[9,11,14] With advances in technology and widespread access to the Internet, telepathology is increasingly being used around the world, improving rapid sharing of cases and access to expert pathologists. Telepathology can be used for remote-site interpretation of all types of pathology material including, but not limited to, H&E stained paraffin tissue sections, frozen sections, cytology or hematology slides, microbiology specimens, clinical fluids (e.g. urine), electron micrographs, electrophoresis gels, and cytogenetics images.[2,15,16,17,18,19,20,21,22,23,24] In practice, these digital images are typically linked to patient information including identification/medical record numbers, clinical history, and relevant laboratory and radiology data.[25] Table 1 summarizes milestones of the many technological advances in telepathology.[14] The primary modes of telepathology include static imaging, dynamic imaging, hybrid static/dynamic telepathology, and WSI. Tabel 1 Telepathology system classification[14]
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.081 | 0.056 |
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".