Characterizing Variability in Lung Cancer Outcomes and Influence of a Lung Diagnostic Assessment Program in Southeastern Ontario, Canada
Bibliographic record
Abstract
Introduction: Regional variability in lung cancer (LC) outcomes exists across Canada, including in the province of Ontario. The Lung Diagnostic Assessment Program (LDAP) in southeastern (SE) Ontario is a rapid-assessment clinic that expedites the management of patients with suspected LC. We evaluated the association of LDAP management with LC outcomes, including survival, and characterized the variability in LC outcomes across SE Ontario. Methods: We conducted a population-based retrospective cohort study by identifying patients with newly diagnosed LC through the Ontario Cancer Registry (January 2017–December 2019) and linked to the LDAP database to identify LDAP-managed patients. Descriptive data were collected. Using a Cox model approach, we compared 2-year survival for patients managed through LDAP vs. non-LDAP. Results: We identified 1832 patients, 1742 of whom met the inclusion criteria (47% LDAP-managed and 53% non-LDAP). LDAP management was associated with a lower probability of dying at 2 years (HR 0.76 vs. non-LDAP, p < 0.0001). Increasing distance from the LDAP was associated with a lower likelihood of LDAP management (OR 0.78 for every 20 km increase, p < 0.0001). LDAP-managed patients were more likely to receive specialist assessment and undergo treatments. Conclusions: In SE Ontario, initial diagnostic care provided via LDAP was independently associated with improved survival in patients with LC.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".