Primary Clonal Loss of Mismatch Repair Protein on Immunohistochemistry: A Pattern of Abnormality That Warrants Genetic Workup
Bibliographic record
Abstract
Article Tools CASE REPORTS Article Tools OPTIONS & TOOLS Export Citation Track Citation Add To Favorites Rights & Permissions COMPANION ARTICLES No companion articles ARTICLE CITATION DOI: 10.1200/PO.22.00111 JCO Precision Oncology no. 6 (2022) e2200111. Published online June 14, 2022. PMID: 35700411 Primary Clonal Loss of Mismatch Repair Protein on Immunohistochemistry: A Pattern of Abnormality That Warrants Genetic Workup Christine Orr , MD1,2xChristine OrrSearch for articles by this author; Chiyun Wang , MD1xChiyun WangSearch for articles by this author; Canan Firat , MD1xCanan FiratSearch for articles by this author; Louise C. Connell , MBBCh3xLouise C. ConnellSearch for articles by this author; Margaret R. Sheehan, MS3xMargaret R. SheehanSearch for articles by this author; Efsevia Vakiani, MD, PhD1xEfsevia VakianiSearch for articles by this author; Zsofia K. Stadler , MD3xZsofia K. StadlerSearch for articles by this author; and Jinru Shia , MD1xJinru ShiaSearch for articles by this author Show More 1Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY2Department of Pathology and Molecular Medicine, Queen's University, Kingston, Ontario, Canada3Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY https://doi.org/10.1200/PO.22.00111 First Page Full Text PDF Figures and Tables © 2022 by American Society of Clinical OncologySUPPORTSupported in part by National Cancer Institute Grant No. P30 C008748 and by the Romeo Milio Lynch Syndrome Foundation.AUTHOR CONTRIBUTIONSConception and design: Louise C. Connell, Jinru ShiaAdministrative support: Jinru ShiaProvision of study materials or patients: Margaret R. Sheehan, Efsevia Vakiani, Jinru ShiaCollection and assembly of data: Christine Orr, Chiyun Wang, Canan Firat, Margaret R. Sheehan, Efsevia Vakiani, Zsofia K. Stadler, Jinru ShiaData analysis and interpretation: Christine Orr, Zsofia K. Stadler, Jinru ShiaManuscript writing: All authorsFinal approval of manuscript: All authorsAccountable for all aspects of the work: All authorsAUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTERESTThe following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated unless otherwise noted. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO's conflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/po/author-center.Open Payments is a public database containing information reported by companies about payments made to US-licensed physicians (Open Payments).Zsofia K. StadlerThis author is a member of the JCO Precision Oncology Editorial Board. Journal policy recused the author from having any role in the peer review of this manuscript.Consulting or Advisory Role: Adverum, REGENXBIO, Gyroscope, Neurogene, AlconJinru ShiaConsulting or Advisory Role: Paige.AINo other potential conflicts of interest were reported.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".