Comparison of elapsed times from breast cancer detection to first adjuvant therapy in a Canadian province, 1999–2000 and 2003–2004
Bibliographic record
Abstract
6005 Background: Wait times for cancer treatment are of significant concern to the Canadian public. Previous studies of time to breast cancer care have examined single care events such as time to surgery. The spectrum of care however extends from disease detection to completion of adjuvant therapies. Our objective was to compare elapsed times from breast cancer detection to start of first adjuvant therapy in a Canadian province over 2 time periods; 1999–2000 and 2003–2004. Methods: A retrospective chart review was performed. All eligible women had pathologic confirmation of invasive disease and were referred to a provincial cancer center between Sept 1, 1999-Sept 1, 2000 (cohort 1) and Sept 1, 2003-Sept 1, 2004 (cohort 2). All dates were abstracted from patient charts and electronic records. The log-rank test was used to assess differences in time to events between cohorts. Results: Care intervals assessed (cohort 1 vs 2, median days); (I) detection to pathologic confirmation; 13d vs 14d (p=.12) (II) pathologic confirmation to definitive surgery; 21d vs 24d (p=.002) (III) definitive surgery to referral receipt at cancer center; 16d vs 19d (p=.005) (IV) referral receipt to patient contact; 10.5d vs 6d (p=.04) (V) patient contact to first med/rad onc appt; 5d vs 6d (p=.48) (VI) first appt to start of first adjuvant therapy 11d vs 18.5d (p=.03). Summary elapsed times are presented below with interquartile ranges (IQR). Conclusions: The majority of women experience long elapsed times for breast cancer care. Elapsed times have lengthened over the cohorts studied, although the overall difference did not reach statistical significance. Intervals prior to referral receipt at cancer centers account for prolongation in elapsed times between cohorts. Our data, spanning a sequence of care events, more fully elucidates waiting time burden and may provide a framework for the design and evaluation of programs aimed at reducing elapsed care times. No significant financial relationships to disclose.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".