Assessing Access to And Outcomes of Medical Oncology and Radiation Oncology Consultation in Non-Small Cell Lung Cancer Patients: A Population-Based Study Using Administrative Data
Bibliographic record
Abstract
Background: Therapeutic advances in non-small cell lung cancer (NSCLC) have shifted treatment away from chemotherapy towards immunotherapy, monoclonal antibody, and tyrosine kinase inhibitor therapy. Most studies focusing on access to specialist care and lung cancer treatment were conducted before novel therapeutic strategies in NSCLC. This study aimed to better understand and inform referral practices for patients with NSCLC in Ontario. Methods: A retrospective population-based study using linked administrative healthcare data was conducted between 2010 and 2019. The study cohort was defined as patients, aged 18 years of age or older, with a stage I to IV NSCLC diagnosis in Ontario. Primary outcome: medical oncology or radiation oncology consultation within 120 days of diagnosis. Prognostic factors for consultation and receipt of treatment were identified using logistic regression. Results: 73,849 patients were diagnosed with NSCLC with 61.3% and 50.9% receiving a medical oncology or radiation oncology consultation respectively. The median time to consultation was 24 days (interquartile range [IQR] 13-49 days). As the stage increased, consultation was more likely (odds ratio [OR] 6.07, 95% CI 5.78-6.38). As the distance to the nearest cancer center increased consultation was less likely (OR 0.72, 95% CI 0.67-0.78). Stage III NSCLC and patients aged 40-44 years were more likely to receive treatment OR 4.09 (95%CI 3.82-4.38) and OR 3.28 (95% CI 2.51-4.28) respectively. Conclusion: Even in a universal health care system, socioeconomic factors impact a patient’s access to specialist care. Given newer, more effective therapeutic options for NSCLC, access to specialist care must be equitable.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| 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) | 0.001 | 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".