Referral to a cancer center (CC), medical oncologist (MO), and use of systemic treatment (ST) in stage IV non-small cell lung cancer (NSCLC) patients: Comparison of years 2003-2006 and 2010 cohorts in a single Canadian institution.
Bibliographic record
Abstract
e19056 Background: Despite evidence for survival improvement, only about 25% of stage IV NSCLC patients receive ST. Factors affecting referral to CC, MO, and use of ST are largely unknown. Methods: Stage IV NSCLC patients in Southern Alberta were identified from the provincial cancer registry. Baseline characteristics, referral to CC and MO, and reasons for referral/treatment decisions, were collected from electronic charts. Multivariate analyses were performed to compare median overall survival (mOS), referral and treatment patterns in two cohorts. Results: 925 patients from 2003-2006 and 261 from 2010 were included (n=1186): median age 68.2 yrs (range 32-96). In 2003-2006, more patients were referred to CC (94.3 vs. 82.8%, p<0.0001), but less received traditional chemotherapy (TC) than in 2010 (22.2 vs. 43.1%, p<0.0001). Referral to CC/MO and use of TC correlated with survival (p<0.0001): mOS 16.0 months in those receiving TC vs. 3.7 months in those not referred to CC. Patients who received TC were younger than those who were not referred to CC (p<0.0001). Reasons for no referral to MO, including rapid decline (30.5%), patient wish (16.4%), poor ECOG (12.5%), and a decision to manage symptoms only (10.6%), were similar to those for foregoing TC. EGFR status correlated significantly with referral and TC only in 2010 cohort. Metastasis pattern (M1a or M1b) and pulmonary embolism correlated significantly with referral and TC only in 2003-2006 cohort. Distance to CC did not impact referral or TC (p=0.56). Among patients referred to MO, 88 (17.2%) and 26 (23.2%) patients received biologics in the 2003-2006 and 2010 cohorts, respectively, with mOS of 22.5 months in 2003-2006 and 15.2 months in 2010 (Log-rank p=0.13). Conclusions: Our experience confirms that ST improves mOS yet has low uptake in stage IV NSCLC. Most factors limiting ST uptake appear non-modifiable. While a more rapid referral process may provide patients access to ST before deterioration, greater availability of well tolerated drugs for all NSCLC patients will likely be the most important factor in increasing ST uptake in this population.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| 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".