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Testing for clonal hematopoiesis of indeterminate potential in breast cancer survivors.

2021· article· en· W3168906818 on OpenAlexaff
Tal Sella, Geoffrey Fell, Peter G. Miller, Christopher J. Gibson, Shoshana M. Rosenberg, Craig Snow, Daniel G. Stover, Kathryn J. Ruddy, Jeffrey Peppercorn, Lidia Schapira, Virginia F. Borges, Steven E. Come, Ellen Warner, Donna Neuberg, Benjamin L. Ebert, Ann H. Partridge

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
FundersV Foundation for Cancer Research
KeywordsMedicineBreast cancerInternal medicineMalignancyCancerPopulationOncologySurgery

Abstract

fetched live from OpenAlex

e24108 Background: Clonal hematopoiesis of Indeterminate Potential (CHIP) is associated with adverse clinical outcomes including increased risk of hematologic malignancies and heart disease. Limited data suggest an increased prevalence of CHIP in patients treated for solid tumors, particularly after exposure to radiation and chemotherapy. CHIP testing may inform risk-reduction strategies for cancer survivors. Little is known about patient knowledge, attitudes, and preferences regarding CHIP testing. Methods: We surveyed survivors without history of recurrence participating in an ongoing prospective cohort study of young women with breast cancer (BC). The survey was sent by email and included an introduction to CHIP including risk factors and clinical associations. Respondents then reviewed a vignette and were asked about CHIP testing preferences (definitely/probably test vs. definitely/probably not test) considering sequentially: 1) population-based 10-year risk of BC recurrence, hematological malignancy and heart disease; 2) estimated increase in these risks with CHIP; 3) current CHIP management; 4) a dedicated CHIP clinic; and 5) a theoretical CHIP treatment. Changes in preferences from the prior scenario were evaluated with the McNemar's test using a type I error rate of 5%. Results: 528/642 (82.2%) eligible women responded to the survey, at a median age of 46 (range: 31-54) years (median time from diagnosis: 108 months (range: 60-168)), and 88% were white. Most had stage 1/2 BC (78.8%) and had received chemotherapy (73.1%) and/or radiation (61.9%). 93.6% had never heard of CHIP prior to survey. After initial patient vignette presentation, most women (87.1%,) recommended CHIP testing if offered. Preferences for testing decreased (p<0.05) when considering population-based risks, with 11.1% shifting their preference from CHIP testing to not testing. After considering increased risks associated with CHIP, interest in testing increased (p<0.05), with 10.1% shifting their preference to testing. Interest significantly (p<0.05) increased with the possibility of managing CHIP through a clinic or a hypothetical CHIP treatment, with 7.2% and 14.1% switching their preferences towards testing, respectively. Finally, 75.8% responded that they themselves, after learning about CHIP and reviewing the vignette, would want to have CHIP testing; 28.2% reported that learning about CHIP and the associated risks caused them at least moderate anxiety. Conclusion: Few young BC survivors were aware of CHIP yet most indicated an interest in testing after learning about it. Testing preferences were influenced by risks presented and potential management strategies. Findings highlight the importance of effective risk communication and the need for adequate psychosocial support when considering testing for CHIP and other potential clinical biomarkers predictive of cancer and other medical risks in cancer survivors.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.130
GPT teacher head0.489
Teacher spread0.359 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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