Variation in breast cancer grading in 1,636 resections assessed using control charts and in silico kappa
Bibliographic record
Abstract
OBJECTIVE: Assess interpretative variation in Nottingham grading using control charts (CCs) and in silico kappa (ISK). METHODS: In house invasive breast cancer cases (2011-2019) at two institutions with a synoptic report were extracted. Pathologist interpretative rates (PIRs) were calculated and normed for Nottingham grade (G) and its components (tubular score (TS), nuclear score (NS), mitotic score (MS)) for pathologists interpreting >35 cases. ISKs were calculated using the ordered mutually exclusive category assumption (OMECA) and maximal categorical overlap assumption (MCOA). RESULTS: The study period included 1,994 resections. Ten pathologists each assessed 38-441 cases and together saw 1,636; these were further analyzed. The PIR medians (normed ranges) were: G1:24%(18-27%), G2:53%(43-56%) and G3:26%(19-33%). The MCOA ISK and the number of statistical outliers (p< 0.05/p< 0.001) to the group median interpretive rate (GMIR) for the ten pathologists was G1: 0.82(2/0 of 10), G2: 0.76(1/1), G3: 0.71(3/1), TS1: 0.79(1/0), TS2: 0.63(5/1), TS3: 0.66(5/1), NS1: 0.37(5/4), NS2: 0.60(4/3), NS3: 0.59(4/4), MS1: 0.78(3/1), MS2: 0.78(3/1), MS3: 0.77(2/0). The OMECA ISK was 0.62, 0.49, 0.69 and 0.71 for TS, NS, MS and G. CONCLUSIONS: The nuclear score has the most outliers. NS1 appears to be inconsistently used. ISK mirrors trends in conventional kappa studies. CCs and ISK allow insight into interpretive variation and may be essential for the next generation in quality.
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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 | 0.024 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 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".