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Record W7112544970

Pre-diagnostic delays for symptomatic breast cancer in Canada: A scoping review

2025· other· W7112544970 on OpenAlexaboutno aff

Bibliographic record

VenueOSF Preprints (OSF Preprints) · 2025
Typeother
Language
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsBreast cancerPsychological interventionCancerPresentation (obstetrics)Stage (stratigraphy)MEDLINEIntervention (counseling)
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND AND RATIONALE: Breast cancer is the most common cancer among Canadian women and a leading cause of morbidity and mortality. Timely diagnosis remains a critical cornerstone of effective breast cancer care, as diagnostic delays are associated with worse outcomes such as advanced stage and decreased survival. Despite having established screening programs in Canada, a significant proportion of breast cancer patients still present symptomatically and follow a care pathway from first symptom to diagnosis that involves complex patient, provider, and system interactions. The first possible delay in the pre-diagnostic phase for symptomatic breast cancer patients is from symptom to first presentation to healthcare, and is often termed the “patient interval.” In Ontario and the rest of Canada, the patient interval is poorly captured in administrative cancer data due to lack of proxy-metrics and is also inconsistently recorded in clinical/chart data. Determinants of this delay and its relative contribution to later stage presentation and worse outcomes are also not well understood in Canada. This scoping review will map existing Canadian evidence on pre-diagnostic delays, focusing on the first symptom to presentation interval, and how these delays are measured and influenced by socioecological domains (individual, interpersonal, community, organizational, policy/enabling environment). Findings from this review will inform future research, contribute to the development of targeted interventions aimed at reducing pre-diagnostic delays, and support policy and clinical strategies to promote earlier diagnosis and reduce inequities within Canadian breast cancer care pathways. REVIEW OBJECTIVE: 1. To map and synthesize existing evidence on pre-diagnostic intervals using a socioecological framework with a focus on symptom to presentation delays for patients with symptomatic breast cancer in Canada, including how these delays are measured, their duration, and the individual, social, and system-level predictors of pre-diagnostic delays. REVIEW QUESTION: Among adults diagnosed with symptomatic breast cancer in Canada, what is known about the duration, determinants, and conceptualization of pre-diagnostic delays (from first symptom onset to diagnosis)? METHODS: This study will follow the Arkey and O'Malley (2005) and Joanna Briggs Institute (JBI) scoping review methodology (Peters et al., 2020) and be reported according to the PRISMA-ScR guidelines. ELIGIBILITY CRITERIA: • Population: Adults with symptomatic breast cancer (male or female). • Exposure: Measurement of any time interval proceeding diagnosis. • Outcomes: Duration of patient interval; determinants of delay at socio-ecological levels; equity-related factors; disease stage/survival • Study design: Quantitative, qualitative, or mixed-methods primary studies. • Setting: Canadian provinces and territories. • Timeframe: 2000–present (reflecting contemporary diagnostic pathways and organized screening programs). • Language: English or French. INFORMATION SOURCES: MEDLINE (Ovid), Embase, CINAHL, PsycINFO, Web of Science, grey literature The search strategy will include controlled vocabulary and free-text terms related to breast cancer, diagnostic interval/delay, and Canada. A librarian or information specialist will peer-review the strategy following PRISMA-S standards. DATA MANAGEMENT: Search results will be imported into EndNote for deduplication followed by Covidence for screening and data extraction. Two reviewers will independently screen all titles/abstracts and full texts. Discrepancies will be resolved by discussion or a third reviewer. DATE EXTRACTION: Data will be extracted using a pilot-tested form, capturing: • Study details (year, province, design, sample size) • Population characteristics • Definitions of intervals and methods of measurement • Reported duration and determinants of patient interval • Delay related outcomes (e.g. stage, subsequent delays, survival, patient-reported) • Application of the Aarhus checklist criteria • Conceptual or theoretical frameworks used CRITICAL APPRAISAL: While formal risk of bias assessment is not required for scoping reviews, methodological quality and reporting consistency will be evaluated using the Aarhus checklist, which assesses clarity of interval definitions, start and endpoints, and measurement accuracy. This will guide interpretation and identify methodological gaps. DATA SYNTHESIS: Findings will be summarized narratively and presented in evidence tables and evidence gap maps. Quantitative results (e.g., median or mean interval durations) will be tabulated. Determinants will be organized thematically across socioecological levels. DISSEMINATION AND KNOWLEDGE TRANSLATION: The results of this study will be relevant to clinicians, researchers, policy makers, and advocacy groups. Dissemination strategies will include publication in a peer-reviewed journal, presentation at relevant national conferences, and sharing or results with provincial cancer agencies/stakeholders, breast cancer networks, patient advocacy groups, and primary care organizations. Further, the results of this study will inform future thesis work including development of a prospective mixed-method study to evaluate patient delays in breast cancer in Ontario.

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)0.010
metaresearch head score (Gemma)0.073
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.118
Threshold uncertainty score0.418

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.073
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0190.033
Science and technology studies0.0030.002
Scholarly communication0.0050.003
Open science0.0040.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.012
GPT teacher head0.285
Teacher spread0.273 · 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 designSystematic review
Domainnot available
GenreReview

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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