The effect of diagnostic assessment programs on the diagnosis and treatment of patients with lung cancer in Ontario, Canada
Bibliographic record
Abstract
INTRODUCTION: Diagnostic assessment programs (DAPs) were implemented in Ontario, Canada, to improve the efficiency of the lung cancer care continuum. We compared the efficiency and effectiveness of care provided to patients in DAPs relative to usual care (non-DAPs). METHODS: Lung cancer patients diagnosed between 2014 and 2016 were identified from the Ontario Cancer Registry. Using administrative databases, we identified various health-care encounters 6 months before diagnosis until the start of treatment and compared utilization patterns, timing, and overall survival between DAP and non-DAP patients. RESULTS: = 0.0006), and were more likely to have early-stage disease (36% vs. 25%) than non-DAP patients. Although DAP patients had a similar time until diagnosis as non-DAP patients, the time until treatment was 8.5 days shorter for DAP patients. DAP patients were more likely to receive diagnostic tests and specialist consultations and less likely to have duplicate chest imaging. DAP patients were more likely to receive brain imaging. Among early-stage lung cancers, brain imaging was high (74% for DAP and 67% for non-DAP), exceeding guideline recommendations. After adjustment for clinical and demographic factors, DAP patients had better overall survival than non-DAP patients (hazard ratio [HR]: 0.79 [0.76-0.82]), but this benefit was lost after adjusting for emergency presentation (HR: 0.96 [0.92-1.00]). A longer time until treatment was associated with better overall survival. CONCLUSION: DAPs provided earlier treatment and better access to care, potentially improving survival. Quality improvement opportunities include reducing unnecessary or duplicate testing and characterizing patients who are diagnosed emergently.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".