A population-based study of health care system factors associated with receipt of treatment and treatment intent in stage III non-small cell lung cancer.
Bibliographic record
Abstract
146 Background: Stage III non-small cell lung cancer (NSCLC) is a disease with a spectrum of anatomic extent, patient health status, and treatment approaches. When health care quality is optimal, receipt of treatment and its intent should be independent of health system factors. We investigated whether modifiable health care system-level factors are associated with receipt of treatment and treatment intent in stage III NSCLC. Methods: This was a population-based, retrospective cohort study using health administrative data covering nearly the whole population of Ontario, Canada (15 million) from 2010-2018 for people with AJCC 7 or 8 stage III NSCLC aged ≥20 years. System factors were: treatment era, diagnostic interval, health region of residence, travel distance, and volume of advanced radiotherapy and systemic therapy. The health region is responsible for administering regional cancer care. The relative risk (RR) of (1) any treatment versus no treatment, and (2) palliative-intent versus curative-intent treatment was determined, using multivariable Poisson regression models. We adjusted for patient, disease, and treatment factors, including age, sex, rurality, income quintile, substage, comorbidity, histology, and use of PET imaging. Results: 7,093 people with stage III NSCLC diagnosed between 2010 and 2018 were identified. There were differences between groups in patient, disease, and treatment factors. For example, factors associated with no treatment include advanced age (e.g. adjusted RR [95% confidence interval]: 80+ vs. 20-64, 0.83 [0.80-0.87]), greater Elixhauser comorbidity score (e.g. 3+ vs. 0, 0.88 [0.84-0.92]), dementia (RR: 0.78 [0.70-0.87]), palliative care consultation (RR: 0.92 [0.89-0.94]) and geriatrics consultation (RR: 0.82 [0.71-0.95]) (all p<0.05). On multivariable stepwise analysis adjusting for these factors, no system factors were associated with receipt of treatment versus no treatment. For those treated, patient, disease, and treatment factors associated with palliative intent were similar. Over time, there was increasing utilization of immunotherapy and advanced radiotherapy (e.g., VMAT, IMRT) (treatment eras: 2010-2012 vs. 2013-2015 vs. 2016-2018). The major system factor associated with palliative intent treatment amongst those treated was health region of residence (RR: ranges from 0.88 to 1.67, p<0.001), which remained after stratifying analysis by treatment era. Conclusions: Even with increasing adoption of advanced radiotherapy and systemic therapy over time, health region of residence emerged as the major health system-level factor associated with choice of treatment intent for stage III NSCLC after adjusting for patient, disease, and treatment factors. Our study suggests possible opportunities to improve care outcomes by addressing unexplained regional variation in care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".