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Abstract P5-19-07: Defining priorities for research: Interim results of the Canadian metastatic breast cancer priority setting partnership

2018· article· en· W2793038494 on OpenAlexaffabout
Nancy Nixon, C. Simmons, Julie Lemieux, Seema Verma

Bibliographic record

VenueCancer Research · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsMedicineInterimPsychosocialFamily medicineBreast cancerPopulationGeneral partnershipHealth professionalsHealth careDiseaseCancerInternal medicinePsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

Abstract Background: Research priorities are generally determined by funders and researchers without direct involvement and input from patients and caregivers. Certain disease areas have incorporated the patient voice to determine patient driven priorities. In this study, this approach was employed to better understand the needs and priorities of metastatic breast cancer patients and their caregivers. Methods: This study was conducted using methodology outlined by the James Lind Alliance. A steering committee of patients, physicians, patient advocates, and allied health care professionals was assembled to oversee the research study. The initial survey collected unanswered research questions from patients, caregivers, and clinicians. Responses were collected and categorized by consensus of the steering committee. Here we present the results from the national survey. Results: Between November 2016 and April 2017, 733 responses from 311 individuals were collected (62% patients, 11% physicians, 9% caregivers or relatives, 5% nurses/allied health professionals, 2% patient organization representatives, and 10% other). The main themes for key patient priorities are: 136 (19%) related to treatment and monitoring, 78 (11%) linked lifestyle and alternative therapy, 58 (8%) regarded tumour biology, 53 (7%) regarded psychosocial aspects, 46 (6%) to diagnosis, 35 (5%) to toxicity, 24 (3%) to prevention, and 17 (2%) to young or pre-menopausal population. Two hundred and eighty-six (39%) were considered out of scope. The most frequently identified priorities included the role of alternative therapies for improving survival, the role of immune therapy for treating metastatic breast cancer, and the potential for improving outcomes with early detection/surveillance with modern treatment and diagnostic modalities. Conclusion: Patient derived research priorities in advanced breast cancer point to an improved understanding of alternative therapies, integration of immune therapy and a focus on early detection of relapse. These priorities should be addressed by the research community to meet the needs of our patients with advanced breast cancer. Citation Format: Nixon N, Simmons C, Lemieux J, Verma S. Defining priorities for research: Interim results of the Canadian metastatic breast cancer priority setting partnership [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P5-19-07.

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)0.096
metaresearch head score (Gemma)0.071
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.151
Threshold uncertainty score0.984

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0960.071
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0150.003
Scholarly communication0.0100.003
Open science0.0040.011
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.340
GPT teacher head0.443
Teacher spread0.103 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2018
Admission routes2
Has abstractyes

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