Virtual microscopy using whole-slide imaging as an enabler for teledermatopathology: A paired consultant validation study
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
BACKGROUND: There is a need for telemedicine, particularly in countries with large geographical areas and widely scattered low-density communities as is the case of the Canadian system, particularly if equality of care is to be achieved or the difference gap is to be narrowed between urban centers and more peripheral communities. AIMS: 1. To validate teledermatopathology as a diagnostic tool in under-serviced areas; 2. To test its utilization in inflammatory and melanocytic lesions; 3. To compare the impact of 20× (0.5 μm/pixel) and 40× (0.25 μm/pixel) scans on the diagnostic accuracy. MATERIALS AND METHODS: A total of 103 dermatopathology cases divided into three arms were evaluated by two pathologists and results compared. The first arm consisted of 79 consecutive routine cases (n=79). The second arm consisted of 12 inflammatory skin biopsies (n=12) and the third arm consisted of 12 melanocytic lesions (n=12). Diagnosis concordance was used to evaluate the first arm. Whereas concordance of preset objective findings were used to evaluate the second and third arms. RESULTS: The diagnostic concordance rate for the first arm was 96%. The concordance rates of the objective findings for the second and third arms were 100%. The image quality was deemed superior to light microscopy for 40× scans. CONCLUSION: The current scanners produce high-resolution images that are adequate for evaluation of a variety of cases of different complexities.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 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