HER-2/<i>neu</i> Serum Levels Vis-à-Vis Hormonal Response in Metastatic Breast Cancer
Bibliographic record
Abstract
Article Tools SPECIAL DEPARTMENTS Article Tools OPTIONS & TOOLS Export Citation Track Citation Add To Favorites Rights & Permissions COMPANION ARTICLES No companion articles ARTICLE CITATION DOI: 10.1200/JCO.2002.99.002 Journal of Clinical Oncology - published online before print September 21, 2016 PMID: 12149314 HER-2/neu Serum Levels Vis-à-Vis Hormonal Response in Metastatic Breast Cancer Lawrence C. PanascixLawrence C. PanasciSearch for articles by this author A. LiptonxA. LiptonSearch for articles by this author , S.M. AlixS.M. AliSearch for articles by this author , K. LeitzelxK. LeitzelSearch for articles by this author , L. EnglexL. EngleSearch for articles by this author , V. ChinchillixV. ChinchilliSearch for articles by this author Show More Jewish General Hospital, McGill University, Montreal, QuebecPenn State College of Medicine Hershey, PA https://doi.org/10.1200/JCO.2002.99.002 First Page Full Text PDF Figures and Tables © 2002 by American Society of Clinical OncologyjcoJ Clin OncolJournal of Clinical OncologyJCO0732-183X1527-7755American Society of Clinical OncologyResponse01082002In Reply:In response to Dr Panasci’s question, we have performed a 2 × 2 χ2 analysis and a multivariate analysis within the two strata of visceral disease and nonvisceral disease. Based on the χ2 tests, clinical benefit (complete response + partial response + stable) within each stratum was significantly less for serum HER-2/neu elevated versus serum HER-2/neu normal patients (P = .0001 and P = .0011, respectively). Likewise, based on the multivariate analyses, serum HER-2/neu remained a significant prognostic factor for response to therapy within the two strata (P = .0005 and P = .002, respectively).
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".