Role of RHAMM within the hierarchy of well-established prognostic factors in colorectal cancer
Bibliographic record
Abstract
OBJECTIVE: To compare the independent prognostic effect of a panel of immunohistochemical protein markers in colorectal cancer (CRC) and determine their ranking among the established prognostic factors T stage, N stage, vascular invasion, tumour budding and tumour grade. DESIGN: A tissue microarray of 1420 CRCs was immunostained for 23 markers and mismatch repair (MMR) proteins. Immunoreactivity was assessed semi-quantitatively. Receiver operating characteristic (ROC) curves were used to determine cut-off scores for tumour marker positivity. Survival time was investigated for each marker in multivariable analysis with T stage, N stage, vascular invasion, tumour budding and tumour grade. The hazard ratio (HR) was used to compare the prognostic effect of each marker on 5 year survival. RESULTS: To the standard prognostic features, only six markers added independent prognostic information including receptor for hyaluronic acid mediated motility (RHAMM) (HR = 2.39 (1.88 to 3.05)), epidermal growth factor receptor (HR = 1.65 (1.31 to 2.09)), tumour infiltrating lymphocytes (HR = 0.7 (0.54 to 0.92)), urokinase plasminogen activator (HR = 1.38 (1.09 to 1.75)), Raf-1 kinase inhibitor protein (HR = 0.75 (0.58 to 0.96)) and mammalian sterile 20-like kinase 1 (MST1) (HR = 0.75 (0.58 to 0.95). Diffuse (>90% staining) expression of RHAMM ranked above T stage, vascular invasion, tumour budding and tumour grade in terms of adverse prognostic significance and was associated with distant metastasis (p = 0.012) and with worse outcome in patients with metastatic disease (p = 0.031). CONCLUSIONS: The strong adverse effect of RHAMM on outcome in addition to its position within the hierarchy of well-established prognostic factors suggest that RHAMM should be considered a more important prognosticator than tumour grade, tumour budding and vascular invasion in patients with CRC.
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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 | 0.000 | 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 | 0.000 |
| 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".