Proposal of Novel Binary Grading Systems for Cervical Squamous Cell Carcinoma
Bibliographic record
Abstract
We compared grading systems and examined associations with tumor stroma and survival in patients with cervical squamous cell carcinoma. Available tumor slides were collected from 10 international institutions. Broders tumor grade, Jesinghaus grade (informed by the pattern of tumor invasion), Silva pattern, and tumor stroma were retrospectively analyzed; associations with overall survival (OS), progression-free survival (PFS), and presence of lymph node metastases were examined. Binary grading systems incorporating tumor stromal changes into Broders and Jesinghaus grading systems were developed. Of 670 cases, 586 were reviewed for original Broders tumor grade, 587 for consensus Broders grade, 587 for Jesinghaus grade, 584 for Silva pattern, and 556 for tumor stroma. Reproducibility among grading systems was poor (κ = 0.365, original Broders/consensus Broders; κ = 0.215, consensus Broders/Jesinghaus). Median follow-up was 5.7 years (range, 0-27.8). PFS rates were 93%, 79%, and 71%, and OS rates were 98%, 86%, and 79% at 1, 5, and 10 years, respectively. On univariable analysis, original Broders ( P < 0.001), consensus Broders ( P < 0.034), and Jesinghaus ( P < 0.013) grades were significant for OS; original Broders grade was significant for PFS ( P = 0.038). Predictive accuracy for OS and PFS were 0.559 and 0.542 (original Broders), 0.542 and 0.525 (consensus Broders), 0.554 and 0.541 (Jesinghaus grade), and 0.512 and 0.515 (Silva pattern), respectively. Broders and Jesinghaus binary tumor grades were significant on univariable analysis for OS and PFS, and predictive value was improved. Jesinghaus tumor grade ( P < 0.001) and both binary systems (Broders, P = 0.007; Jesinghaus, P < 0.001) were associated with the presence of lymph node metastases. Histologic grade has poor reproducibility and limited predictive accuracy for squamous cell carcinoma. The proposed binary grading system offers improved predictive accuracy for survival and the presence of lymph none metastases.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".