Abstract P4-11-10: Time dependence of biomarkers: Non-proportional effects of immunohistochemical panels predicting relapse risk in early breast cancer
Bibliographic record
Abstract
Abstract Background: We investigate the impact of follow-up duration to determine whether two immunohistochemical prognostic panels, IHC 4 and Mammostrat, provide information on the risk of early or late distant recurrence using the Edinburgh Breast Conservation Series and the Tamoxifen versus Exemestane Adjuvant Multinational (TEAM) trial. Methods: The multivariable fractional polynomial time (MFPT) algorithm was used to determine which variables had possible non-proportional effects. The performance of the scores was assessed at various lengths of follow-up and Cox regression modelling performed over the intervals 0-5 years and > 5 years. Results: We observed a strong time-dependence of both the IHC4 and Mammostrat scores with their effects decreasing over time. In the first five years of follow-up only, the addition of both scores to clinical factors provided statistically significant information (p<0.05) with increases in R2 between 5 and 6% and increases in D-statistic between 0.16 and 0.21. Conclusion: Our analyses confirm that the IHC4 and Mammostrat scores are strong prognostic factors for time to distant recurrence but this is restricted to the first 5 years after diagnosis. This provides evidence for their combined use to predict early recurrence events in order to select those patients who may/will have benefit from adjuvant chemotherapy. Citation Format: Jacqueline Stephen, Gordon Murray, David Cameron, Jeremy Thomas, Ian Kunkler, Wilma Jack, Gill Kerr, Tammy Piper, Cassandra Brookes, Daniel Rea, Cornelis van de Velde, Annette Hasenburg, Christos Markopoulos, Luc Dirix, Caroline Seynaeve, John Bartlett. Time dependence of biomarkers: Non-proportional effects of immunohistochemical panels predicting relapse risk in early breast cancer [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P4-11-10.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".