Diagnostic concordance between traditional and digital workflows. A study on 1427 prostate biopsies
Bibliographic record
Abstract
Objective. To evaluate intra-observer diagnostic reproducibility using traditional slides (TS) versus whole slide images (WSI).Methods. TS and WSI of 1427 prostatic biopsies (107 consecutive patients) were evalu- ated by a single pathologist. Agreement between readings was evaluated with Gwet’s Agreement coefficient (AC) and Landis and Koch benchmark scale.Results. The positive/negative agreement between the readings was almost perfect (AC1= 0.962; 95% CI[0.949,0.974]), with method independent distribution of discrepan- cies. Among positive biopsies, 212 had identical Gleason score (GS) on TS and WSI and discordant GS in 69 cases (AC2 = 0.932; 95% CI[0.907, 0.956]). Concordant negative and positive patient classification was observed in 39 and 64 cases, respectively; two cases were assigned to the positive group on TS and 2 on WSI configuring an almost perfect agreement (AC1=0.929; 95% C1[0.860, 0.998]). ISUP Grade group (ISUP GG) agreement was evaluated in the 60 concordantly positive cases: in 45 cases it was identical on TS and WSI; in 10 biopsies the discrepancy implied a modification of the assigned ISUP GG of ≤ 1 class and in 5 the discrepancy implied a modification of 2 classes. Gwet’s agreement coefficient was (95% CI [0.834, 0.962]), i. e.: almost perfect agreement.Conclusions. Our data show almost perfect agreement between digital and traditional diagnostic activity in a routine setting, confirming that digital pathology can be safely intro- duced into routine workflows.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".