Menacalc, a quantitative method of metastasis assessment, as a prognostic marker for axillary node-negative breast cancer
Bibliographic record
Abstract
BACKGROUND: Menacalc 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 Menacalc and risk of death from breast cancer. Our goal was to determine if Menacalc could be used as an independent prognostic marker for axillary node-negative (ANN) breast cancer. METHODS: Analysis of the association of Menacalc 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 Menacalc with risk of death after adjustment for HER2 status and clinicopathological tumor features. RESULTS: High Menacalc 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 Menacalc 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 Menacalc in the HER2, basal and luminal molecular subtypes. CONCLUSIONS: Menacalc may serve as an independent prognostic biomarker for the ANN breast cancer patient population.
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.
How this classification was reachedexpand
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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".