P1-11-11: Wait Times for Breast Cancer Care in Manitoba 2009–2010. Time To Face the Challenge.
Bibliographic record
Abstract
Abstract Background: Wait times for patients with breast cancer to receive oncological treatment vary significantly. In Manitoba, the radiation treatment wait times improved in the year 2005 compared to 2001. However, other parts of the patient's journey have lengthened and negated the reduction seen in radiation treatment wait times. Aims: To examine the overall time from disease suspicion to treatment of breast cancer patients from June 2009 to June 2010 and to compare to the previously published wait times. Methods: This population-based retrospective study looked at representative samples of women newly diagnosed with breast cancer. Patients were followed from the time of first presentation, either to their family physician or after a suspicious screening mammogram, to the time adjuvant treatment was started. Each patient's journey was subdivided into different chronological stages. The data was compared to the wait times reported in 2005. Results: 363 patients’ data was collected and analyzed. The median elapsed time in days for each phase of the journey was calculated. Total wait times from suspicion to diagnosis were also calculated. The wait times of most stages of the patients’ journey have worsened when compared to 2005. However, there was some improvement in the diagnostic part of the journey. Delays to surgery and pathology have contributed to lengthening of the total journey. There was also a systematic and repetitive administration delay at each transition. Screen-detected cases had a shorter journey than those who presented through a family physician. Conclusions: In spite of improvements achieved in the wait times at some stages of the journey, the total length of the patient journey has not been shortened. This represents not only individual team failures but also a built in complexity in the health system. In order to achieve a meaningful cut in wait times for breast cancer patients, the entire trajectory of care needs to be addressed. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P1-11-11.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".