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The Progress Tracker Breast Cancer Registry: Feasibility of a longitudinal patient-led, patient-reported outcomes (PROM) registry.

2024· article· en· W4399280425 on OpenAlexafffundabout
Shaniah Leduc, Amanda Williams Gibson, Michelle L. Dean, Kimberly Carson, Omar Khan, Doris Howell

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Calgary
FundersBreast Cancer Society of Canada
KeywordsMedicinePromCancer registryBreast cancerCancerPatient registryMedical physicsInternal medicineObstetrics

Abstract

fetched live from OpenAlex

e23169 Background: A breast cancer diagnosis has the greatest disability-adjusted life years lost of all cancers and significant impact on physical, psychoemotional, social and overall global functioning. Directed by Breast Cancer Canada, a registered non-profit patient-led organization and data managed by the University of Calgary’s POET Program, PROgress Tracker uses a novel, peer-to-peer support model for recruitment, engagement and retention, and is the first national longitudinal and non-interventional PROMs registry. Our aim is to demonstrate the feasibility and potential of this registry in centering patient voice and lived experience to transform breast cancer management. Methods: PROgress Tracker, with 10-year enrollment goal of 50,000 Canadians with Stage 0-IV breast cancer, extends a series of validated PROMs (PROMIS, BREAST-Q, FACT-B, GAD, PHQ-9, COST-FACIT, ISI and ESASr-CA) via a digital platform every 3 months for up to 10 years. Dynamic customization of additional PROMs (PRO-CTCAE, BREAST-Q) based on patient and treatment-specific trigger questions assess evolving components of wellbeing, including financial stress, work life and adverse treatment effects. Results: Recruitment began in October 2023. 162 participants from all Canadian regions including remote/rural geographic areas have shared baseline demographic and clinical data including stage, genetic/molecular tumor markers, treatment type(s) and completed an initial series of PROMs. Comprehensive PROMs including sensitive topics (finances, sexuality, mental health) were completed with minimal missing data ( < 5%); 3-month follow-up surveys show a current retention rate of 57%. Preliminary analysis: median age of participants 54 yrs, 95% identify as Caucasian, and 57% are currently working. 46% reported financial stress (99% response rate); 63% reported ongoing symptoms requiring follow-up. 6% were BRCA+ and 19% triple negative, 7% experienced recurrence, 3% Stage IV, 75% received systemic and/or radiation therapy, 38% received targeted therapy. Conclusions: The lived experience of breast cancer is integral to patient-centred change in the era of precision/personalized medicine and is captured by PROgress Tracker using a diverse set of periodic PROMs to understand the evolution of global measures of wellbeing over time. Initial data indicates that this novel peer to peer model via digital administration of comprehensive and longitudinal PROMs is feasible. This initiative demonstrates attaining this scope of data is achievable, has interest and participation from the national breast cancer community and growing capacity to accrue important, real-time data to inform best practice and identify additional supports required for breast cancer care. While early feasibility is evident, further outreach and recruitment initiatives will ensure diversity of participants.

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)0.018
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.404
Threshold uncertainty score0.803

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.028
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.004

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 head0.211
GPT teacher head0.512
Teacher spread0.301 · 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 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

Citations1
Published2024
Admission routes3
Has abstractyes

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