Poorly differentiated clusters in colorectal cancer: a current review and implications for future practice
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
Poorly differentiated clusters (PDC), defined as small groups of ≥5 tumour cells without glandular differentiation, have gained recent attention as a promising prognostic factor in colorectal cancer (CRC). Numerous studies have shown PDC to be significantly associated with other adverse histopathological features and worse clinical outcomes. PDC may hold particular promise in stage II colon cancer, where risk stratification plays a critical role in patient selection for adjuvant chemotherapy. In addition, emerging evidence suggests that PDC can predict lymph node metastasis in endoscopically resected pT1 CRC, potentially helping the selection of patients for oncological resection. In 'head-to-head' comparisons, PDC grade has consistently outperformed conventional histological grading systems both in terms of risk stratification and reproducibility. With a number of large-scale studies now available, this review evaluates the evidence regarding the prognostic significance of PDC, considers its relationship with other emerging invasive front prognostic markers (such as tumour budding and stroma type), assesses its 'practice readiness', addressing issues such as interobserver reproducibility, scoring methodologies and special histological subtypes (e.g. micropapillary and mucinous carcinoma), and draws attention to ongoing challenges and areas in need of further study. Finally, emerging data on the role of PDC in non-colorectal cancers are briefly considered.
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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 | 0.000 | 0.000 |
| 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