American Telemedicine Association clinical guidelines for telepathology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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]
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it