Mena[superscript calc], a quantitative method of metastasis assessment, as a prognostic marker for axillary node-negative breast cancer
Bibliographic record
Abstract
Background Mena[superscript calc] is an immunofluorescence-based, quantitative method in which expression of the non-invasive Mena protein isoform (Mena11a) is subtracted from total Mena protein expression. Previous work has found a significant positive association between Mena[superscript calc] and risk of death from breast cancer. Our goal was to determine if Mena[superscript calc] could be used as an independent prognostic marker for axillary node-negative (ANN) breast cancer. Methods Analysis of the association of Mena[superscript calc] with overall survival (death from any cause) was performed for 403 ANN tumors using Kaplan Meier survival curves and the univariate Cox proportional hazards (PH) model with the log-rank or the likelihood ratio test. Cox PH models were used to estimate hazard ratios (HRs) for the association of Mena[superscript calc] with risk of death after adjustment for HER2 status and clinicopathological tumor features. Results High Mena[superscript calc] was associated with increased risk of death from any cause (P = 0.0199, HR (CI) = 2.18 (1.19, 4.00)). A similarly elevated risk of death was found in the subset of the Mena[superscript calc] cohort which did not receive hormone or chemotherapy (n = 142) (P = 0.0052, HR (CI) = 3.80 (1.58, 9.97)). There was a trend toward increased risk of death with relatively high Mena[superscript calc] in the HER2, basal and luminal molecular subtypes. Conclusions Mena[superscript calc] may serve as an independent prognostic biomarker for the ANN breast cancer patient population.
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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 | 0.003 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".