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Abstract P5-04-04: Does reducing the frequency of regularly scheduled physical examinations affect recurrence detection in patients with early breast cancer?

2023· article· en· W4322775239 on OpenAlexaff
Ana-Alicia Beltran-Bless, Bader Alshamsan, Mashari Alzahrani, John Hilton, Kelly-Anne Baines, Vicky Samuel, Gregory R. Pond, Lisa Vandermeer, Mark Clemons, Gail Larocque

Bibliographic record

VenueCancer Research · 2023
Typearticle
Languageen
FieldMedicine
TopicCancer Diagnosis and Treatment
Canadian institutionsMcMaster UniversityOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineBreast cancerCancerSurvivorship curveInternal medicine

Abstract

fetched live from OpenAlex

Abstract Purpose: Follow-up care of patients with early breast cancer (EBC) usually includes routinely scheduled physical examinations. While ASCO guidelines recommend a physical exam every three to six months for the first three years, there is little evidence to support this schedule. Health care systems continue to be challenged to meet the future growth in demand from increasing numbers of diagnosed patients and long-term survivors, scarcity of health care workers, and the need to control health care costs. Despite recognition that new follow-up models are needed, there continues to be no generally accepted well follow-up strategy. We evaluated recurrence detection patterns of patients transferred into a single centre survivorship program that follows ASCO recommendations. Methods: Consecutive patients with EBC referred to the Wellness Beyond Cancer Program (WBCP) between February 1, 2013, and January 1, 2019, who had breast cancer recurrence, were reviewed. Descriptive analyses were used to present patients and disease characteristics stratified by type of recurrence and mode of cancer detection. Results: Of 206 recurrences, 135 were distant recurrences (65.5%), 41 were ipsilateral breast recurrences (19.9%), and 30 were contralateral new breast cancers (14.6%). Patient reported symptoms lead to the detection of the majority of distant recurrences (125/135, 92.6%). The most common symptoms of recurrence were bone pain (24.8%), dyspnea/cough (13.1%), abdominal pain (10.7%). Ipsilateral breast recurrences were both quite frequently detected by patients (22/41, 53.7) and by routine mammographic surveillance (17/21, 41.5%). Contralateral breast cancers were primarily detected by imaging 83.3% (25/30). Only 2/206 (1.14%) recurrences/new primaries were detected by a healthcare provider at routinely scheduled follow-up visits. There was a statistical difference in recurrence detection between image detected vs. self-detected in the following factors: grade 3 (26.5% vs 51%, p < 0.007), triple negative breast cancer (3.9% vs. 15.1%, p=0.03), and HER2 disease (18.4% vs. 9.8%, p= 0.04). Conclusions: Despite regularly scheduled in-person follow-up visits following ASCO guidelines, healthcare providers rarely detect recurrences. Our data suggests that 30,000 – 35, 000 follow-up visits were required for the healthcare providers to detect these 2 recurrences. This leads to further need for proper survivorship programs with patient and provider education, and concentration on targeted surveillance. Provided patients attend regular screening tests, our data points to less frequent in-person follow-up being associated with non-inferior breast cancer-related outcomes. Future prospective studies are required looking at different models of follow-up. SABCS Abstract Table Citation Format: Ana-Alicia Beltran-Bless, Bader I. Alshamsan, Mashari Alzahrani, John Hilton, Kelly-Anne Baines, Vicky Samuel, Gregory R. Pond, Lisa Vandermeer, Mark Clemons, Gail Larocque. Does reducing the frequency of regularly scheduled physical examinations affect recurrence detection in patients with early breast cancer? [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P5-04-04.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.158
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.043
GPT teacher head0.376
Teacher spread0.333 · 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.

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
Published2023
Admission routes1
Has abstractyes

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