Clinical and prognostic factors associated with diagnostic wait times by breast cancer detection method
Bibliographic record
Abstract
INTRODUCTION: Although prognostic differences between screen-detected, interval and symptomatic breast cancers are known, factors associated with wait times to diagnosis among these three groups have not been studied. METHODS: Of the 16,373 invasive breast cancers diagnosed between January 1, 1995 and December 31, 2003 in a cohort of Ontario women aged 50 to 69, a random sample (N = 2,615) were selected for chart abstraction. Eligible women were classified according to detection method; screen-detected (n = 1181), interval (n = 319) or symptomatic (n = 406). Diagnostic wait time was calculated from the initial imaging or biopsy to breast cancer diagnosis. Logistic regression analysis examined associations between diagnostic wait times dichotomized as greater or less than the median and demographic, clinical and prognostic factors separately for each detection cohort. RESULTS: Women who underwent an open biopsy had significantly longer than median wait times to diagnosis, compared to women who underwent a fine needle aspiration or core biopsy; (screen-detected OR = 2.76, 95% CI = 2.14-3.56; interval OR = 2.56, 95% CI = 1.50-4.35; symptomatic OR = 5.56, 95% CI = 3.33-9.30). Additionally, screen-detected breast cancers diagnosed with stage II and symptomatic cancers diagnosed at stage III or IV had significantly shorter diagnostic wait times compared to those diagnosed at stage 1 (OR = 0.66 95% CI = 0.50-0.87 and OR = 0.46, 95% CI = 0.25-0.85 respectively). CONCLUSIONS: Our study is consistent with expedited diagnostic work-up for breast cancers with more advanced prognostic features. Furthermore, women who had an open surgical biopsy had a greater than the median diagnostic wait time, irrespective of detection method.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".