How advanced lung cancer patients are really treated at the population level? The Ontario, Canada experience
Bibliographic record
Abstract
Background: Clinical trials define treatment recommendations but patients in the real world may be unable or unwilling to undergo treatments with demonstrated efficacy in fit patients. The Canadian Partnership Against Cancer has developed a model of lung cancer (LC) management (OncoSim-lung) in 2008 based on clinical trials data and expert advice. To credibly project the future clinical and economic impacts of cancer control measures using OncoSim, the model has been refined using real-world data. Methods: Treatment data by histology and stage were extracted from the Ontario Cancer Registry for LC cohorts diagnosed in 2010 and 2013. All incident cases that satisfied the IARC rule of a new primary were included. Missing or unknown stage cases were excluded. Clinical pathways were validated by oncologists from different disciplines across Canada. Results: The 2013 cohort included 8,086 staged LC: NSCLC (n = 7,143) Stage I 18.7%, II 8%, III/IIIa 11.4%, IIIb 4.9% IV 56.8%; SCLC (n = 943) limited 67.7%, extensive 32.3%. Of 813 stage III/IIIa patients, only 26% underwent surgery, 41% of whom received adjuvant chemotherapy or postoperative radical radiotherapy (16%); 13% received trimodality treatment. Of the 75% of Stage III not receiving surgery, 26% had NAT and 21% had palliative radiotherapy alone. Of those receiving active treatment, 20% received combined chemo +radiotherapy and 13% each had chemotherapy alone or radical radiotherapy alone. Of 356 stage IIIb patients, 17% had NAT, 28% received palliative radiotherapy and only 30% had chemo + radical radiotherapy. 18% had chemo alone. Of 4055 stage IV NSCLC, 47% had NAT, 24% received chemotherapy alone and 23% had palliative radiotherapy only. Of those who received first-line chemotherapy (n = 1059), 47% received second line chemotherapy and of those, 37% received third line therapy. Conclusions: Compared to prior expert opinion, there was a much lower frequency of chemo-radiotherapy in Stage III disease and a higher frequency of NAT across all stages of disease. The updated OncoSim model will now have a credible real-world base from which the impacts of new treatment interventions on survival and budget impact can be better estimated. Legal entity responsible for the study: Canadian Partnership Against Cancer. Funding: Canadian Partnership Against Cancer. Disclosure: All authors have declared no conflicts of interest.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".