Telepathology for Routine Light Microscopic and Frozen Section Diagnosis
Bibliographic record
Abstract
Telepathology (TP) uses telecommunication linkages to electronically capture, store, retrieve, and transmit images to distant sites. We assessed the feasibility of a dynamic real-time TP system for light microscopic (LM) diagnosis of anatomic pathology specimens, including frozen sections. Six pathologists, in 2 separate periods, read a set of 160 retrospectively retrieved slides (80 of which were frozen sections) by TP and LM. Reading times were recorded. Diagnoses were compared with the reference diagnosis (established by a group of 5 independent pathologists) and graded on a scale of 0 to 2 (2, correct; 1, incorrect but no clinical impact; 0, incorrect with clinical impact). Overall, LM was more accurate than TP compared with the reference diagnosis (score, 1.68 vs 1.54). There was no difference in accuracy between frozen section and paraffin-embedded tissue. Intraobserver agreement ranged from 82.5% to 88.2%. The average reading time was 6.0 minutes for TP and 1.4 minutes for LM. During the study, reading time decreased for TP but not for LM. These results show that despite marginally lower accuracy and longer reading times, TP isfeasible for routine light microscopic diagnosis, including frozen sections.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 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".