Lung Cancer Care Trajectory at a Canadian Centre: An Evaluation of How Wait Times Affect Clinical Outcomes
Bibliographic record
Abstract
BACKGROUND: Lung cancer continues to be one of the most common cancers in Canada, with approximately 28,400 new cases diagnosed each year. Although timely care can contribute substantially to quality of life for patients, it remains unclear whether it also improves patient outcomes. In this work, we used a set of quality indicators that aim to describe the quality of care in lung cancer patients. We assessed adherence with existing guidelines for timeliness of lung cancer care and concordance with existing standards of treatment, and we examined the association between timeliness of care and lung cancer survival. METHODS: Patients with lung cancer diagnosed between 2010 and 2015 were identified from the Pulmonary Division Lung Cancer Registry at our centre. RESULTS: We demonstrated that the interdisciplinary pulmonary oncology service successfully treated most of its patients within the recommended wait times. However, there is still work to be done to decrease variation in wait time. Our results demonstrate a significant association between wait time and survival, supporting the need for clinicians to optimize the patient care trajectory. INTERPRETATION: It would be helpful for Canadian clinicians treating patients with lung cancer to have wait time guidelines for all treatment modalities, together with standard definitions for all time intervals. Any reductions in wait times should be balanced against the need for thorough investigation before initiating treatment. We believe that our unique model of care leads to an acceleration of diagnostic steps. Avoiding any delay associated with referral to a medical oncologist for treatment could be an acceptable strategy with respect to reducing wait time.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".