The Chemotherapy Response Score (CRS): Interobserver Reproducibility in a Simple and Prognostically Relevant System for Reporting the Histologic Response to Neoadjuvant Chemotherapy in Tuboovarian High-grade Serous Carcinoma
Bibliographic record
Abstract
A 3-tier histopathologic scoring system, the chemotherapy response score (CRS), was previously devised for reporting the histologic response to neoadjuvant chemotherapy in interval debulking surgery specimens of stage IIIc/IV tuboovarian high-grade serous carcinoma. This has been shown to predict the outcome and offer additional information to other methods of assessing the treatment response. In the present study, the reproducibility of this scoring system was assessed by determining the interobserver agreement among reporting pathologists. A total of 5 groups each comprising 3 pathologists with different levels of expertise were selected. The participants underwent an online tutorial on how to apply the CRS system. 40 cases (38 cases in 2 appraiser groups) were scored individually by each of the 15 pathologists. The interobserver reproducibility was calculated using Fleiss' κ, Kendall's coefficient of concordance, and the absolute agreement between (a) individual pathologists within 1 group, (b) with the majority score agreement between all groups, and (c) with all individual scores. The CRS system was found to be highly reproducible among all the pathologists' groups (κ=0.761). The agreement in identifying the group of patients with the best response to chemotherapy was exceptionally high (κ=0.926). We conclude that CRS has a high interobserver reproducibility, especially in identifying the subgroup of patients with the best chemotherapy response, justifying its inclusion in clinical trials and reporting practice.
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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 | 0.027 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".