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Abstract PS7-47: Real-world treatment patterns in older patients with stage I-III breast cancer: A population-based study

2021· article· en· W3133096119 on OpenAlexaffabout
Atul Batra, Shiying Kong, Rodrigo Rigo, Winson Y. Cheung

Bibliographic record

VenueCancer Research · 2021
Typearticle
Languageen
FieldMedicine
TopicMultiple and Secondary Primary Cancers
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineInterquartile rangeBreast cancerCancer registryStage (stratigraphy)Internal medicineProportional hazards modelRadiation therapyCancerComorbidityPopulationLogistic regressionHazard ratioConfidence interval

Abstract

fetched live from OpenAlex

Abstract Background: The number of older patients is increasing globally. However, older patients are less likely to be offered participation in clinical trials. We aimed to assess the real-world treatment patterns in older patients with breast cancer and to examine the associations of advancing age with cancer specific survival (CSS) and overall survival (OS). Methods: Patients aged > 65 years and diagnosed with stage I-III breast cancer in a large Canadian province from 2004 to 2017 were identified. Data from administrative sources were linked with the provincial cancer registry. Patients were categorized based on their age at diagnosis: old (65-74 years), older (75-84 years) and oldest (> 85 years). Logistic regression analyses were performed to determine the associations of age with receipt of surgery, chemotherapy, radiotherapy, and hormone treatment. Kaplan-Meier survival curves were plotted to estimate the 5-year CSS and OS. Cox proportional hazards models were constructed to examine the associations of age with CSS and OS, adjusting for stage and treatment. Results: A total of 10,719 older patients were eligible. The median age was 73 (interquartile range, 68-79) years and 99.2% were women. There were 6,057 (56.5%) old, 3,438 (32.1%) older, and 1,224 (11.4%) oldest patients. The oldest patients were more likely to have a higher Charlson comorbidity index (CCI) score (<.001) and present with stage III disease (P<.001). Further, the oldest patients were least likely to be treated with surgery (80.2% vs 98.2%, P<.001), chemotherapy (0.8% vs 24.7%, P<.001), radiotherapy (14.6% vs 56.8%, P<.001) and hormone therapy (41.8% vs 66.8%, P<.001) compared with old women with breast cancer. In multivariable logistic regression analyses, the older and oldest patients had a lower likelihood of surgery (odds ratio [OR], 0.42; 95% confidence interval [CI],0.33-0.53; P<.001 and OR, 0.10; 95% CI, 0.08-0.13; P<.001), chemotherapy (OR, 0.08; 95% CI, 0.06-0.09; P<.001 and OR, 0.01; 95% CI, 0.01-0.02; P<.001), radiotherapy (OR, 0.50; 95% CI, 0.46-0.55; P<.001 and OR, 0.13; 95% CI, 0.11-0.15; P<.001) and hormone treatment (OR, 0.61; 95% CI, 0.56-0.67; P<.001 and OR, 0.31; 95% CI, 0.27-0.35; P<.001). There were 1,504 breast cancer related deaths and 1,845 deaths due to other causes. At a median follow-up of 4.9 years, the 5-year CSS rates were 90.7%, 84.1% and 74.9% (P<.001), while 5-year OS rates were 86.2%, 71.3% and 43.2% (P<.001) for the old, the older and the oldest patients. After adjusting for stage and treatment, advancing age predicted for worse CSS (older; hazards ratio [HR], 1.33; 95% CI, 1.17-1.50; P<.001, oldest; HR, 1.49; 95% CI, 1.26-1.76; P<.001) and worse OS (older; HR, 1.82; 95% CI, 1.67-1.98; P<.001, oldest; HR, 3.13; 95% CI, 2.82-3.48; P<.001). Conclusions: Although all treatment modalities were administered less frequently with advancing age, a more significant decline was noted for adjuvant therapy than surgery. The worse CSS observed in the advanced age groups suggest a potential role for cancer-directed therapy in improving outcomes. Further research should focus on the development of less toxic treatment strategies in geriatric patients with breast cancer. Citation Format: Atul Batra, Shiying Kong, Rodrigo Rigo, Winson Y Cheung. Real-world treatment patterns in older patients with stage I-III breast cancer: A population-based study [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS7-47.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.061
GPT teacher head0.407
Teacher spread0.346 · 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 teacher head, not a consensus.

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".

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Citations0
Published2021
Admission routes2
Has abstractyes

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