Regression grading in neoadjuvant treated pancreatic cancer: an interobserver study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIM: Several regression grading systems have been proposed for neoadjuvant chemoradiation-treated pancreatic ductal adenocarcinoma (PDAC). This study aimed to examine the utility, reproducibility and level of concordance of three most frequently used grading systems. METHODS: Four gastrointestinal pathologists used the College of American Pathologists (CAP), Evans, MD Anderson Cancer Centre (MDA) regression grading systems to grade 14 selected cases (7-20 slides from each case) of neoadjuvant chemoradiation-treated PDAC. A postscoring discussion with each pathologist was conducted. The results were entered into a standardised data collection form and statistical analyses were performed. RESULTS: There was little concordance across the three systems. The Kendall coefficient of concordance agreement scores were: CAP: 2-poor, 2-fair; Evans: 1-fair, 1-moderate, 2-good; MDA: 1-poor, 2-moderate, 1-good. Interpretation in all three grades in the CAP grading system was a source of discrepancy. Furthermore, using fibrosis as a criterion to assess regression was contentious. In the Evans system, quantifying tumour destruction using arbitrary percentage cut-offs (ie, 9% vs 10%; 50% vs 51%, etc) was imprecise and subjective. Although the MDA system generated greatest concordance, this was due to 'oversimplification' surrounding wide, arbitrarily assigned thresholds of </> 5% of tumour. CONCLUSIONS: All systems lacked precision and clarity for accurate regression grading. Presently the clinical utility and impact of histological regression grading in patient management is questionable. There is a need to re-evaluate regression grading in the pancreas and establish a reproducible, clinically relevant grading system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it