Factors Influencing the Timeliness and Completeness of Appropriate Staging Investigations for Patients with Stage I–III Lung Cancer in Southeastern Ontario
Bibliographic record
Abstract
(1) Background: Comprehensive and timely lung cancer (LC) staging is essential for prognosis and management. The Lung Diagnostic Assessment Program (LDAP) in Southeastern (SE) Ontario aims to provide rapid, guideline-concordant care for suspected LC patients. We evaluated factors affecting the completeness and timeliness of staging for stage I–III LC patients in SE Ontario, including the impact of LDAP management. (2) Methods: This was a population-based retrospective cohort study using the LDAP database (January 2017–December 2019), linked with the Ontario Cancer Registry, to identify newly diagnosed LC patients. A Cox model approach identified variables associated with staging completeness and timeliness. (3) Results: Among 755 patients, 459 (60.8%) were managed through LDAP. Optimal staging was achieved in 596 patients (78.9%), 23 (3.0%) had alternative staging, and 136 (18.0%) had incomplete staging. In the adjusted analyses, LDAP management was associated with a higher likelihood of complete staging (OR 2.29, p < 0.0001) and faster staging completion (β = −18.53, p < 0.0001). Increased distance to PET centres was associated with a longer time to complete staging (β = 8.95 per 100 km, p = 0.0007), as was longer time to diagnosis (β = 21.63 per 30 days, p < 0.0001). (4) Conclusions: LDAP management in SE Ontario significantly improved staging completeness and shortened staging time for stage I–III LC 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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".