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Record W3041190423 · doi:10.7326/m19-3481

Clinical Benefits, Harms, and Cost-Effectiveness of Breast Cancer Screening for Survivors of Childhood Cancer Treated With Chest Radiation : A Comparative Modeling Study.

2020· article· en· W3041190423 on OpenAlexaff
Jennifer M. Yeh, Kathryn P. Lowry, Clyde B. Schechter, Lisa Diller, Oğuzhan Alagöz, Gregory T. Armstrong, John M. Hampton, Wendy M. Leisenring, Qi Liu, Jeanne S. Mandelblatt, Diana L. Miglioretti, Chaya S. Moskowitz, Kevin C. Oeffinger, Amy Trentham‐Dietz, Natasha K. Stout

Bibliographic record

VenuePubMed · 2020
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsUniversity of Alberta
FundersNational Cancer Institute
KeywordsMedicineBreast cancerMammographyBreast cancer screeningCancerPopulationCancer screeningPediatricsInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Surveillance with annual mammography and breast magnetic resonance imaging (MRI) is recommended for female survivors of childhood cancer treated with chest radiation, yet benefits, harms, and costs are uncertain. OBJECTIVE: To compare the benefits, harms, and cost-effectiveness of breast cancer screening strategies in childhood cancer survivors. DESIGN: Collaborative simulation modeling using 2 Cancer Intervention and Surveillance Modeling Network breast cancer models. DATA SOURCES: Childhood Cancer Survivor Study and published data. TARGET POPULATION: Women aged 20 years with a history of chest radiotherapy. TIME HORIZON: Lifetime. PERSPECTIVE: Payer. INTERVENTION: Annual MRI with or without mammography, starting at age 25, 30, or 35 years. OUTCOME MEASURES: Breast cancer deaths averted, false-positive screening results, benign biopsy results, and incremental cost-effectiveness ratios (ICERs). RESULTS OF BASE-CASE ANALYSIS: Lifetime breast cancer mortality risk without screening was 10% to 11% across models. Compared with no screening, starting at age 25 years, annual mammography with MRI averted the most deaths (56% to 71%) and annual MRI (without mammography) averted 56% to 62%. Both strategies had the most screening tests, false-positive screening results, and benign biopsy results. For an ICER threshold of less than $100 000 per quality-adjusted life-year gained, screening beginning at age 30 years was preferred. RESULTS OF SENSITIVITY ANALYSIS: Assuming lower screening performance, the benefit of adding mammography to MRI increased in both models, although the conclusions about preferred starting age remained unchanged. LIMITATION: Elevated breast cancer risk was based on survivors diagnosed with childhood cancer between 1970 and 1986. CONCLUSION: Early initiation (at ages 25 to 30 years) of annual breast cancer screening with MRI, with or without mammography, might reduce breast cancer mortality by half or more in survivors of childhood cancer. PRIMARY FUNDING SOURCE: American Cancer Society and National Institutes of Health.

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)0.013
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.038
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.011
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0030.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.000

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.

Opus teacher head0.153
GPT teacher head0.374
Teacher spread0.220 · 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 designSimulation or modeling
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

Citations37
Published2020
Admission routes1
Has abstractyes

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