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Abstract P6-05-12: Understanding Patient Perspectives on Window of Opportunity Clinical Trial Participation in Breast Cancer

2023· article· en· W4322774476 on OpenAlexaff
Vanessa Lopez Ozuna, Gregory R. Pond, John M.S. Bartlett, Lazlo Radvanyi, Melanie Spears, Teresa Petrocelli, Carol Gordon, Rebecca Rose, Angel Arnaout

Bibliographic record

VenueCancer Research · 2023
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsResearch CanadaUniversity of TorontoOntario Institute for Cancer ResearchMcMaster UniversityOttawa Hospital
Fundersnot available
KeywordsMedicineBreast cancerClinical trialCancerWindow of opportunityOncologyFamily medicineInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background: Window of opportunity (WOO) clinical trials take advantage of the waiting period between a patient’s cancer diagnosis and standard treatment (usually surgery) to evaluate novel cancer therapies and their biologic effects in vivo. These types of trials are being increasingly harnessed in the clinical setting for the safe and rapid evaluation of novel therapeutic strategies in treatment naive patients, thereby expediting drug development. Distinct from neoadjuvant trials, no therapeutic benefit is envisaged and the patient’s standard treatments are not intentionally delayed. The purpose of this study was to understand the patient motivations and perspectives for participating in WOO trials. Methods: This study was conducted at an academic cancer center where two breast cancer WOO trials were ongoing (NCT04781725 and NCT04676516). Eligible patients with newly diagnosed operable invasive breast cancers participating in either of these WOO trials were recruited to this separate study. Patients were provided with a questionnaire that surveyed their motivation and perspectives for participation or lack of participation in the WOO trial Results: From April 2021- May 2022, the study recruited 89 patients with age ranging from of 40-78 yrs with tumors ranging from 1.5-4.3 cm. Surgical wait times ranged from 2-8 weeks. Of the 83 patients that participated in a WOO trial, the most common reasons for participation included (a) the potential to benefit other patients in the future (90%) (b) trust in their treating doctor (88%), (c) desire to contribute to scientific research (62%) and (d) a belief that they may benefit from the therapy (39%). For these patients, 49% reported that the possibility of a repeat biopsy would not deter them from trial participation; whereas 11% said that it definitely would. Of the 6 patients that chose not to participate in a WOO trial the most common reasons included (a) travel or transportation issues (50%) and (b) lack of belief of potential benefit to them (33%). For these patients, when asked whether the participation of a cancer patient in the design of the WOO trial would change their mind, all reported that it would not make a difference. Conclusion: WOO trials are becoming increasingly common in oncology research. Understanding patient perspectives for WOO trial participation is useful to inform trial design and communication approaches in future WOO trial efforts. Citation Format: Vanessa Lopez Ozuna, Gregory R. Pond, John MS Bartlett, Lazlo Radvanyi, Melanie Spears, Teresa Petrocelli, Carol Gordon, Rebecca J. Rose, Angel Arnaout. Understanding Patient Perspectives on Window of Opportunity Clinical Trial Participation in 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 P6-05-12.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.843
Threshold uncertainty score0.710

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.596
GPT teacher head0.583
Teacher spread0.013 · 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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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