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Abstract P4-04-11: Managing Wait Time Between Surgery and Treatment Initiation Plan in Patients with Breast Cancer: a Mixed-method Approach

2025· article· en· W4411289215 on OpenAlexaboutno aff
Wissam Saliba, Enam Alsrayheen, Francesco Dellorusso

Bibliographic record

VenueClinical Cancer Research · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBreast cancerCancerSurgeryInternal medicine

Abstract

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Abstract Oncotype DX test (ODX) is a genomic testing that is considered a standard of care requirement for a subset of breast cancer (BC) patients, meeting specific histopathological criteria, facilitating informed decisions regarding their personalized systemic treatment. However, the conventional process for ordering this test often delays the initiation of systemic therapy, as it was only ordered after the first Medical Oncology Consultation. This study evaluates a novel initiative implemented at a medium-sized cancer center providing care for rural and underserved communities in Nova Scotia-Canada. The initiative aimed at minimizing the time interval between surgery and treatment onset, through a phone call by the Medical Oncologist (MO) directly engaging the patient, explaining the test and treatment options, securing their consent for immediate ordering. Employing a mixed-method design, this study assessed wait times at various intervals along the patient’s journey from surgery to treatment initiation. Qualitatively, semi-structured phone interviews were conducted with purposefully selected 11 breast cancer patients to explore their experiences and satisfaction with the MO's communication regarding the ODX test, focusing on achieving patient-centred care and alleviating distress during the waiting period. Results reveal that 180 breast cancer patients underwent ODX testing between July 2018 and July 2022, 55% via the standard pathway and 45% through the new intervention. Median wait time from surgery to ODX result availability decreased significantly from 60 [IQR: 49-70] to 46 [IQR: 31-91] days (p-value < 0.001). While the median wait time from MO referral to chemotherapy onset decreased from 50 [IQR: 40-82] to 43 [IQR: 40-63] days, it was not statistically significant (p-value = 0.5). However, there was a notable two-week reduction in median wait time from referral to radiation therapy initiation (p-value = 0.09). Importantly, the median wait time from the initial consultation with the MO to the chemotherapy consent date decreased from 24 to 0 days (p-value < 0.001), indicating immediate consent for chemotherapy treatment during the first consultation. Qualitative findings underscored patient satisfaction with the new intervention, particularly appreciating the clear and mindful information provided by the MO, even through remote discussion. In conclusion, this intervention effectively reduced the interval between surgery and ODX results, facilitating expedited treatment initiation and enabling informed decision-making at the time of the initial consultation. Using plain language during remote discussions further enhanced patient satisfaction and understanding of treatment options, emphasizing the significance of effective communication in patient care. Using this intervention can also enhance ODX utilization in rural communities. Citation Format: Wissam Saliba, Enam Alsrayheen, Yoana Fuentes, Francesco Dellorusso. Managing Wait Time Between Surgery and Treatment Initiation Plan in Patients with Breast Cancer: a Mixed-method Approach [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P4-04-11.

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)0.031
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.166

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.043
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.152
GPT teacher head0.400
Teacher spread0.248 · 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 designQualitative
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
Published2025
Admission routes1
Has abstractyes

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