Validation of A Nationwide Digital Pediatric Pathology Consultation Network
Bibliographic record
Abstract
BACKGROUND: Digital pathology facilitates remote pathology consultations. Pediatric pathologists in Canada formed a nationwide digital pathology consultation network, mostly for second opinion review of pediatric cancer cases. Validation of such a large network for clinical use is challenging. Here we report our unique validation process of this digital pathology network. METHOD: This study was designed in keeping with the College of American Pathologist (CAP) guidelines, and included 14 pathologists from 9 hospitals across Canada. All cases are pediatric pathology cases. Each pathologist reviewed multiple digital cases and the corresponding glass slide cases. For each review, intra-observer concordance (diagnosis on digital case versus diagnosis on glass slide case) was recorded, creating a data point. RESULT: The study generated 269 valid diagnostic data points. Out of the 269 data points, 257 were concordant (95.5% concordance), exceeding the CAP recommendation of 95% concordance. Thus, the network was successfully validated. CONCLUSION: This is a unique validation study for a large nationwide digital pediatric pathology network. The study involved all pathologists/hospitals in the network, closely emulating real world clinical process. The network was successfully validated.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".