RUNX1T1
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
OBJECTIVES: Using gene expression profiling on frozen primary pancreatic endocrine tumors (PETs), we discovered RUNX1T1 as a leading candidate progression gene. This study was designed (1) to validate the differential expression of RUNX1T1 protein on independent test sets of metastatic and nonmetastatic PETs and (2) to determine if RUNX1T1 underexpression in primary tumors was predictive of liver metastases. METHODS: Immunohistochemical expression of RUNX1T1 protein was quantified using Allred scores on archival metastatic (n = 13) and nonmetastatic (n = 24) primary adult PET tissues using custom-designed tissue microarrays. Wilcoxon rank sum/Fisher exact tests and receiver operating characteristic curves were used in the data analysis. RESULTS: Median RUNX1T1 scores were 2 (2-7) and 6 (3-8) in metastatic versus nonmetastatic primaries (P < 0.0001). Eleven of 13 metastatic and 1 of 24 nonmetastatic primaries exhibited RUNX1T1-scores of 4 or less (P < 0.0001). Low RUNX1T1 expression was highly associated with hepatic metastases (P < 0.0001), whereas conventional histological criteria (Ki-67 index, mitotic rate, necrosis) were weakly associated with metastases (P = 0.08-0.15). Considering RUNX1T1 expression (Allred) score of 4 or less to be predictive, the sensitivity to predict hepatic metastases was 85%, with a specificity of 96%. CONCLUSIONS: RUNX1T1 protein is underexpressed in well-differentiated metastatic primary PETs relative to nonmetastatic primaries and emerges as a promising novel biomarker for prediction of liver metastases.
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 | 0.000 | 0.000 |
| 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 | 0.001 |
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