Impact of rates of referral and systemic therapy on 1-year outcomes in metastatic NSCLC: A real-world population based study.
Bibliographic record
Abstract
e18238 Background: EGFR/ALK inhibitors and immunotherapy represent promising treatments for metastatic NSCLC, but patients must be referred to a cancer center (CC) to be considered for these treatments. Local referral rates to a CC for advanced pancreatic cancer are only 51%. We hypothesized that rates of referral for stage IV NSCLC are also low, thereby affecting survival. Methods: Using linked data from the provincial cancer registry, oncology specific EMR, administrative claims and vital statistics, we identified all patients diagnosed with stage IV NSCLC from 2009 to 2016 in Alberta, Canada. Demographics, Charlson Comorbidity Index (CCI), method of diagnosis, year of diagnosis and site of metastasis were compared between patients referred vs not referred (NR) to a CC and between those who received systemic therapy (ST) vs those who did not. A multivariable piecewise constant hazard model was constructed to estimate the hazard ratio (HR) of death in the 1st year. Results: We identified 9717 stage IV NSCLC patients among whom 65.8% and 34.2% were diagnosed pre and post 2012. In this cohort, 6907 (71%) were seen at a CC. Factors which predict referral to a cancer center are: dx by cytology (OR 6.81, 95% CI: 5.99-7.75; p < 0.001) or histology (OR 6.29, 95% CI: 5.40-7.34; p < 0.001) vs radiologic dx; Age < 70 (OR 1.87, 95% CI: 1.69-2.07; p < 0.001); dx after 2012 (OR 1.52, 95% Cl 1.36-1.70; p < 0.001) and CCI≤1 (OR 1.50, 95% Cl 1.34-1.68; p < 0.001). ST was administered to 2057(21.2%). Factors that predict ST are: dx by cytology (OR 6.48, 95% CI: 5.18-8.11; p < 0.001) or histology (OR 6.35.18 , 95% CI: 5.02-8.04; p < 0.001); Age < 70 (OR 2.55, 95% Cl 2.33-2.82; p < 0.001); CCI≤1 (OR 1.56, 95% Cl 1.39-1.76; p < 0.001); dx after 2012 (OR 1.28, 95% CI: 1.16-1.41 and female gender (OR 1.18 , 95% CI: 1.07-1.29; p < 0.001; p < 0.001). Within the 1st year post dx, HR for mortality was lower both in patients referred to a CC vs NR (HR 0.3, 95%CI .28-0.31, p < 0.0001) and patients receiving ST vs no ST (HR 0.3, 95% CI .28-0.32, p < 0.0001). Conclusions: Close to 1 in 3 patients with stage IV NSCLC were not referred to a CC even though referral and receipt of ST were associated with a significantly lower risk of death in the 1st year following diagnosis. Clear delineation and wide dissemination of appropriate referral pathways are needed to improve outcomes, especially among older patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".