Integrating Regional and Community Lung Cancer Services to Improve Patient Care
Bibliographic record
Abstract
Lung cancer is the leading cause of cancer death in Canada. The organization of health care services is central to the delivery of accessible, high-quality medical care and may be one factor that influences patient outcome. An exciting opportunity arose for clinicians to initiate the redesign of lung cancer services provided by three institutions in the Greater Toronto Area. This qualitative report describes the integrated lung cancer network that they developed, the innovation it has facilitated, and the systematic approach being taken to evaluate its impact. Available clinical resources were deployed to restructure services along patient-centred lines and to provide greater access to the specialist lung cancer team. A non-hierarchical clinical network was established that consolidates the lung cancer team. A multi-institutional and multidisciplinary tumour board and comprehensive thoracic oncology clinics are at its core. This innovative organizational paradigm considers all of the available services at each facility and aims to fully integrate specialists across the three institutions, thereby maximizing resource utilization. We believe that this paradigm may have wider applicability. The network is currently working to complete a current program of further service improvements and to objectively assess its impact on patient outcome.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".