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Record W2601749821 · doi:10.1038/bjc.2017.87

Evaluating wait times from screening to breast cancer diagnosis among women undergoing organised assessment vs usual care

2017· article· en· W2601749821 on OpenAlexafffundabout
Anna M. Chiarelli, Derek Muradali, Kristina M. Blackmore, Courtney R. Smith, Lucia Mirea, Vicky Majpruz, Frances P. O’Malley, May Lynn Quan, Claire Holloway

Bibliographic record

VenueBritish Journal of Cancer · 2017
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsHealth Sciences CentreFoothills Medical CentreSt. Michael's HospitalUniversity of TorontoSunnybrook Health Science CentreCancer Care Ontario
FundersCanadian Institutes of Health ResearchCancer Care Ontario
KeywordsMedicineBreast cancerLogistic regressionBiopsyRetrospective cohort studyCancerBreast biopsyAnxietyBreast cancer screeningMammographyGynecologyObstetricsInternal medicine

Abstract

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BACKGROUND: Timely coordinated diagnostic assessment following an abnormal screening mammogram reduces patient anxiety and may optimise breast cancer prognosis. Since 1998, the Ontario Breast Screening Program (OBSP) has offered organised assessment through Breast Assessment Centres (BACs). For OBSP women seen at a BAC, an abnormal mammogram is followed by coordinated referrals through the use of navigators for further imaging, biopsy, and surgical consultation as indicated. For OBSP women seen through usual care (UC), further diagnostic imaging is arranged directly from the screening centre and/or through their physician; results must be communicated to the physician who is then responsible for arranging any necessary biopsy and/or surgical consultation. This study aims to evaluate factors associated with diagnostic wait times for women undergoing assessment through BAC and UC. METHODS: Of the 2 147 257 women aged 50-69 years screened in the OBSP between 1 January 2002 and 31 December 2009, 155 866 (7.3%) had an abnormal mammogram. A retrospective design identified two concurrent cohorts of women diagnosed with screen-detected breast cancer at a BAC (n=4217; 47%) and UC (n=4827; 53%). Multivariable logistic regression analyses examined associations between wait times and assessment and prognostic characteristics by pathway. A two-sided 5% significance level was used. RESULTS: Screened women with breast cancer were two times more likely to be diagnosed within 7 weeks when assessed through a BAC vs UC (OR=1.91, 95% CI=1.73-2.10). In addition, compared with UC, women assessed through a BAC were significantly more likely to have their first assessment procedure within 3 weeks of their abnormal mammogram (OR=1.25, 95% CI=1.12-1.39), ⩽3 assessment procedures (OR=1.54, 95% CI=1.41-1.69), ⩽2 assessment visits (OR=1.86, 95% CI=1.70-2.05), and ⩾2 procedures per visit (OR=1.41, 95% CI=1.28-1.55). Women diagnosed through a BAC were also more likely than those in UC to have imaging (OR=1.99, 95% CI=1.44-2.75) or a biopsy (OR=3.69, 95% CI=2.64-5.15) vs consultation only at their first assessment visit, and two times more likely to have a core or FNA biopsy than a surgical biopsy (OR=2.08, 95% CI=1.81-2.40). Having ⩽2 assessment visits was more likely to reduce time to diagnosis for women assessed through a BAC compared with UC (BAC OR=10.58, 95% CI=8.96-12.50; UC OR=4.47, 95% CI=3.94-5.07), as was having ⩽3 assessment procedures (BAC OR=4.97, 95% CI=4.26-5.79; UC OR=2.95, 95% CI=2.61-3.33). Income quintile affected wait times only in women diagnosed in UC, with those in the two highest quintiles more likely to receive a diagnosis in 7 weeks. CONCLUSIONS: Women with screen-detected breast cancer in OBSP were more likely to have shorter wait times if they were diagnosed through organised assessment. This might be as a result of women diagnosed through a BAC having more procedures per visit, procedures scheduled in shorter intervals, and imaging or biopsy on their first visit. Given the significant improvement in timeliness to diagnosis, women with abnormal mammograms should be managed through organised assessment.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.294
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.063
GPT teacher head0.412
Teacher spread0.350 · 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.

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

Citations34
Published2017
Admission routes3
Has abstractyes

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