MUC1 Gene–Derived Glycoprotein Assays for Monitoring Breast Cancer (CA 15-3, CA 27.29, BR): Are They Measuring the Same Antigen?
Bibliographic record
Abstract
Abstract Context. —There are 2 general types of assays measuring MUC1 gene–derived glycoprotein: assays for cancer antigen (CA) 15-3, which are sandwich assays, and assays for CA 27.29, which are competitive assays. These 2 types of assays measure slightly different parts of this tandem-repeat molecule. Across-method assay differences hinder the exchange of patient test values among integrated health care networks and among countries. Objective. —This report evaluates the method differences among these assays to determine if the differences between these assays are mainly related to variations in calibration or differences in analyte specificity. Design. —Data from 22 College of American Pathologists survey challenges were analyzed to compare 10 commercial assay methods for these 2 related analytes. In addition, data from 58 patient samples were analyzed to compare 3 of these assays. Results. —The linear correlation coefficients comparing the within-method medians of these proficiency test distributions were very high (>0.99) for all of the methods; however, the regression slopes varied from 0.836 to 1.095. The regression slopes for the patient specimens varied similarly, but the correlation coefficients were lower. Conclusions. —This study indicates that many of the test value differences for these measurements are due to differences in assay calibration rather than differences in the specificity of the assay measurement systems. Survey test data potentially could be used to help harmonize these assay differences.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".