Do socioeconomic factors and primary care model affect early breast cancer diagnosis in a cohort of breast cancer patients in an urban Canadian centre?
Bibliographic record
Abstract
Objectives: Studies have shown an association between socioeconomic status (SES) and breast cancer (BC) treatment and diagnosis. We examined the relationship between SES, primary care physician (PCP) model and early detection of BC, as defined by asymptomatic screening and early stage at diagnosis, in a universal healthcare system. Methods: Data were collected for consecutive patients diagnosed with BC from January 2010 to December 2011.Variables included patient and disease factors, type of PCP, stage at diagnosis and method of tumour identification. Area-level SES variables were obtained from 2006 Canadian census data. Multivariable logistic regression was used to identify predictors of early BC diagnosis. Odds ratios with 95% confidence intervals were reported. Results: Results: A total of 721 patients were treated for breast cancer during the 2-year period. Predictors of early diagnosis through screening included: patients aged 51-70 (OR 4.3, 95% CI:2.6-7.2), BMI > 30 (1.5, 1.0-2.3), not employed (0.5, 0.3-0.8), and previous screening within 2 years (3.0, 2.0-4.4). Predictors of diagnosis at an early stage were having a 1st degree relative with breast cancer (2.2, 1.3-3.8) and having screening at an Ontario Breast Screening Program (2.9, 1.6-5.2). Conclusion: Certain patient variables such as age and family history, predicted the likelihood of early detection of BC by asymptomatic screening and diagnosis at an early stage. In our urban cohort of BC patients, SES factors were not found to be predictors of early detection of BC
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".