Benefits of a centralized cancer control program on outcomes: Evaluation of wait times for diagnosis and management of advanced non-small cell lung cancer (NSCLC).
Bibliographic record
Abstract
e19039 Background: Canada has a national cancer registry that tracks patterns across the population; British Columbia (BC) has the lowest lung cancer mortality in the country. The BC Cancer Agency (BCCA) operates five regional cancer centres and community outreach sites that deliver cancer care using evidence-based standards and guidelines established by the BCCA. The province is 945,000 km2; the population is distributed 85% urban and 15% rural. We hypothesize that adherence to provincial cancer control programs results in equitable services in all geographic locations. Methods: A retrospective population-based review of stage IIIb/IV NSCLC patients (pts) diagnosed from Jan 2008 to Dec 2010 referred to the BCCA was done. Pt characteristics and time intervals between diagnosis, referral, oncology consultation and palliative therapy were extracted. The Kruskal-Wallis test was used to compare wait times (WT). The Kaplan-Meier method and log rank test was used for OS. Results: 1,431 pts were identified. Median time from diagnosis (DX) to referral (RF) was similar across all geographic regions (11-13 days). Median time from RF to oncology consultation ranged from 7-16 d. Table 1 describes medical oncology (MO) WT to chemotherapy (CT) and radiation oncology (RO) WT to radiotherapy (RT). Conclusions: While WT varied between key events in the pts’ lung cancer trajectory by geographic location, the overall survival from diagnosis was similar in all groups. Provision of provincially mandated guidelines and care conferred equitable outcomes in advanced NSCLC across BC. [Table: see text]
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".