A novel classification of tumour budding in colorectal cancer based on the presence of cytoplasmic pseudo‐fragments around budding foci
Bibliographic record
Abstract
AIMS: Tumour budding is an adverse prognostic factor in colorectal cancer (CRC). We have investigated the significance of cytoplasmic fragments occurring in the immediate vicinity of tumour budding foci. METHODS AND RESULTS: Seventy-three CRCs with high-grade budding (> 10 budding foci in a x 20 objective field) were classified according to extent of budding (10-19 versus 20+ foci) and by the presence or absence of cytoplasmic fragments identified by immunostaining for cytokeratin. In serial sections, cytoplasmic fragments were shown to be dendritic cell processes in continuity with budding tumour cells and were renamed pseudo-fragments. Cytoplasmic pseudo-fragments, but not extent of budding, were associated with aberrant expression of beta-catenin (P = 0.045) and laminin-5 gamma2 (P < 0.0001), and with absent peritumoral lymphocytic infiltration (P = 0.0077). Cytoplasmic pseudo-fragments had a stronger association with infiltrating growth pattern (P = 0.0014) than extent of tumour budding (P = 0.014). There was no association between extent of budding and cytoplasmic pseudo-fragments (P = 0.12). CONCLUSIONS: Cytoplasmic pseudo-fragments may be a marker for an activated budding phenotype that is associated with cell motility and increased invasiveness in CRC and is independent of the extent of budding.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".